Introduction to neuroimaging 25-26.pdf

# Peripheral measures of physiological responses Peripheral measures assess physiological responses that reflect autonomic nervous system (ANS) activity and are often used as indicators of arousal, emotional states, and cognitive effort in research. These measures are "peripheral" because they are recorded from the body's extremities or surface, rather than directly from the central nervous system [1](#page=1) [3](#page=3). ### 1.1 Skin conductance Skin conductance, also known as electrodermal activity (EDA), primarily reflects sympathetic nervous system (SNS) activity, often associated with the "fight or flight" response. Arousal stimulates sweat glands, increasing the skin's conductivity. It is generally interpreted as an index of arousal intensity rather than the valence (positive or negative) of an emotional or cognitive experience. Higher arousal leads to higher skin conductance [1](#page=1). #### 1.1.1 Measurement method The measurement involves applying two electrodes to the volar (palm side) surfaces of the fingers or palm of the non-dominant hand. A very small voltage is applied, and the amount of current passed is interpreted as skin conductivity. The faster the voltage travels between electrodes, the higher the conductivity. Typical units are microsiemens (µS) or microohms (µmho) [1](#page=1). #### 1.1.2 Different measures of skin conductance * **Skin Conductance Level (SCL):** This is a tonic (slow-changing) measure of skin conductance during a task, reflecting baseline arousal over time. SCL exhibits large interindividual differences, and while absolute values may not be highly meaningful, they are useful for comparing different conditions across time blocks [2](#page=2). * **Non-specific Skin Conductance Response (NS-SCR):** These are spontaneous, phasic changes in electrical conductivity that are not directly related to specific stimuli or the task. They can indicate general arousal or spontaneous reactions [2](#page=2). * **Event-Related Skin Conductance Response (ER-SCR):** This is a phasic response specifically elicited by a particular event or stimulus. ER-SCRs typically have a latency of 1-3 seconds after stimulus presentation and are considered the most interesting measure for experimental manipulation, as they directly reflect the body's reaction to specific stimuli such as images, sounds, or decisions [2](#page=2). > **Tip:** When interpreting SCL, focus on changes across experimental blocks rather than absolute values due to significant interindividual variability. #### 1.1.3 Applications of skin conductance * **Affective picture processing:** Skin conductance is often used in fear conditioning research to quantify the intensity of a person's fear response to conditioned stimuli. For example, an individual with a specific fear (e.g., snakes) will show a higher SCR to a snake than to a spider, indicating selectivity in emotional reactivity. Control groups typically exhibit lower responses across all stimuli [2](#page=2). * **Stop-signal task:** In tasks designed to assess response inhibition, such as the stop-signal task, ER-SCR can reveal higher arousal during stop trials, especially when inhibition fails (Stop Error) compared to successful stop trials or go trials. This indicates arousal linked to cognitive control and inhibition processes [2](#page=2). ### 1.2 Pupillometry Pupil size is influenced by luminance levels but also by fluctuations in the autonomic nervous system, reflecting arousal, surprise, mental effort, and adaptation in uncertain environments. Low luminance or SNS stimulation leads to pupil dilation, while high luminance or parasympathetic nervous system (PNS) stimulation causes pupil constriction [3](#page=3). #### 1.2.1 Underlying mechanism Pupil dilation is directly linked to the firing rate of neuroadrenergic neurons in the locus coeruleus, a region in the brainstem. When these neurons fire, pupil diameter changes accordingly [3](#page=3). #### 1.2.2 Measurement method Pupillometry uses an infrared light source to illuminate the eye and an infrared-sensitive camera to capture the contrast between the pupil and iris. By keeping illuminance constant, changes in pupil size can reflect cognitive processes, such as mental effort, during experimental tasks. It is crucial to ensure no illuminance differences between experimental conditions [3](#page=3). > **Tip:** When designing pupillometry experiments, ensure all stimuli used across different conditions have the same luminance, even if their colors differ. #### 1.2.3 Applications of pupillometry * **Flanker conflict task:** In tasks like the flanker conflict task, where participants judge a central target while ignoring surrounding flankers, pupil size can be elevated in incongruent trials and incorrect trials. Notably, pupil size can be highest in congruent-incorrect trials, suggesting surprise even in an easy trial, leading to a larger pupil response [3](#page=3). #### 1.2.4 Pupillometry vs. Eye-tracking While both use similar setups, pupillometry measures involuntary pupil movements reflecting ANS activity, whereas eye-tracking focuses on controlled eye movements, making it more akin to behavioral measures like reaction time and accuracy [3](#page=3). ### 1.3 Cardiac activity The heart's activity is a key physiological response, most pronounced during exercise but also sensitive to cognitive tasks. The cardiac cycle is initiated by the sino-atrial node (pacemaker) and regulated by both the PNS (slowing down) and SNS (speeding up) [3](#page=3). #### 1.3.1 Main methods and measures * **Electrocardiography (ECG):** Records electrical activity of the heart, yielding measures of heart rate (HR) and heart rate variability (HRV) [4](#page=4). * **Impedance Cardiography (ICG):** Measures changes in electrical conductivity of the thorax, used to derive the pre-ejection period (PEP) [4](#page=4) [5](#page=5). #### 1.3.2 Electrocardiography (ECG) ECG records the heart's electrical activity via surface electrodes, reflecting the production and conduction of action potentials during the cardiac cycle [4](#page=4). ##### 1.3.2.1 Heart Rate (HR) Heart rate is calculated based on the RR interval (time between consecutive R-waves in an ECG) and is typically expressed in beats per minute (bpm). It is sensitive to emotional processes influenced by the SNS and PNS. The formula for HR is [4](#page=4): $$HR = \\frac{60}{\\text{RR interval in seconds}}$$ While physical activity is a major influence, HR is also affected by psychological and cognitive factors such as stress, emotions, and task load [4](#page=4). > **Example:** Emotional states, when induced in a lab setting, typically elevate heart rate, reflecting SNS influence. Conversely, during affective picture processing, heart rate may decrease initially as attention is oriented to emotional stimuli, reflecting PNS influence; however, if emotional events require action or involve fear/stress, HR will increase, reflecting SNS influence [4](#page=4). ##### 1.3.2.2 Heart Rate Variability (HRV) Heart rate variability refers to the temporal variations between consecutive heartbeats (RR intervals or inter-beat intervals). HRV is considered a general measure of PNS influence on the heart. Baseline HRV varies between individuals ("trait"), but it can also be modulated by cognitive demand ("state"). Generally, higher HRV is predictive of better cognitive performance [4](#page=4). #### 1.3.3 Impedance Cardiography (ICG) ICG measures the total electrical conductivity of the thorax and its changes over time. A high-frequency current flows between an electrode pair, and impedance changes are detected by a second pair, generating an impedance pulse wave. This method allows for the derivation of the pre-ejection period (PEP) [4](#page=4) [5](#page=5). ##### 1.3.3.1 Pre-Ejection Period (PEP) The PEP is the time interval from ventricular electrical stimulation to the opening of the aortic valve, reflecting pumping performance. It is thought to be particularly sensitive to SNS influence on the heart and cannot be derived from standard ECG alone. The width of the PEP varies individually [5](#page=5). > **Example:** In subliminal response priming tasks, PEP can be used to assess effort mobilization. Reduced PEP has been associated with better task performance, indicating that participants in a more activated state (shorter PEP) performed better. Monetary incentives can further reduce PEP [5](#page=5). ### 1.4 Respiration Respiratory activity, measured using a belt around the chest, is related to heart rate, with faster respiration leading to a faster heart rate. Respiration is increased by physical activity and also by psychological and cognitive factors like stress and task load. Both SNS and PNS influence respiratory rate, and respiration can be actively used to modulate ANS activity (e.g., slow breathing activates the PNS) [5](#page=5). * * * # Animal research methodologies and their relation to human studies Animal research is critical for interpreting human neuroimaging data by providing high-resolution insights into neuronal activity that are not achievable in human studies [8](#page=8). ### 2.1 Understanding neuronal activity #### 2.1.1 Neuronal resting potential and signal transmission Neuronal activity relies on the sodium-potassium pump, which maintains a negative resting potential by pumping sodium ions (Na+) out and potassium ions (K+) in. Signal transmission between neurons is chemical; neurotransmitters released at synapses open Na+ or K+ channels, causing excitatory or inhibitory stimulation. The summation of these synaptic inputs alters the postsynaptic neuron's membrane potential, leading to an action potential that travels along the axon to activate subsequent neurons [8](#page=8). ### 2.2 Measuring and manipulating neuronal activity Various techniques allow researchers to measure and manipulate neuronal activity at different levels, providing crucial insights that can be extrapolated to human studies [9](#page=9). #### 2.2.1 Recording and inducing neuronal activity **Recording neural activity via electrodes:** Micro-electrodes (1-10µm) can record the electrical activity of single neurons, specifically the voltage generated in the extracellular matrix during an action potential (spikes). These electrodes are typically implanted using a carrier device mounted on the skull and guided by a stereotactic reference frame derived from brain imaging. This allows for recording neural activity while animals are freely moving [9](#page=9). > **Example 1:** Recording from hippocampal neurons revealed that specific neurons fire when an animal is oriented in a particular direction, akin to "head direction cells" found in human hippocampi [9](#page=9). > **Example 2:** Single-cell recordings from dopaminergic neurons in the substantia nigra during reinforcement learning demonstrated distinct firing patterns: increased firing upon reward delivery, increased firing upon a conditioned stimulus (CS) predicting reward, and decreased firing when a predicted reward is omitted. These findings formed the basis for human motivation studies using fMRI and PET [10](#page=10) [9](#page=9). **Inducing neuronal activity via electrodes:** The same electrode setup can be used to stimulate neurons, offering insights into the function of a brain region and its projections [10](#page=10). > **Example:** Electrical stimulation of the septal area in rats, delivered each time the rat pressed a lever, served as an operant reinforcer, with some rats neglecting basic needs for the stimulation. This demonstrated the concept of "pleasure centers" and showed that septal stimulation leads to dopamine release in the nucleus accumbens, similar to primary rewards [10](#page=10). **Inducing neuronal activity via optogenetic imaging:** Optogenetics uses genetically modified neurons expressing light-activated ion channels (opsins) to control and monitor neuronal activity with light in living, freely moving animals. Blue light can activate ON opsins (e.g., channelrhodopsine), while yellow light can activate OFF opsins (e.g., halorhodopsin), allowing researchers to trigger cellular activation or deactivation [10](#page=10). > **Tip:** Optogenetics is not ethically or practically feasible for human studies due to ethical concerns and infection risks [10](#page=10). #### 2.2.2 Pharmacological manipulations and lesions These methods allow for causal conclusions about the function of specific brain regions or neurotransmitter systems [11](#page=11). **Pharmacological manipulations:** Administering receptor agonists or antagonists, or altering neurotransmitter re-uptake and synthesis, can modulate neuronal activity [9](#page=9). > **Example:** Dopamine depletion in rats using 6-hydroxydopamine injections in the nucleus accumbens led to a reduced willingness to exert effort (e.g., climb a barrier) for higher rewards, even though rats could still discriminate between reward densities. This suggests dopamine's role in effort-based motivation rather than solely outcome evaluation [11](#page=11). **Lesions:** Creating targeted lesions, often through toxin injections, can reveal the role of specific brain areas [11](#page=11). > **Example:** Lesions in the anterior cingulate cortex (ACC) of rats in a T-maze task specifically impaired their motivation to overcome an obstacle for a higher reward. However, when a low-effort option was also made available, the lesioned rats normalized their behavior. This indicates that ACC lesions reduce motivation when effort is involved but do not inherently impair motor function or reward discrimination [11](#page=11). #### 2.2.3 Local field potentials (LFPs), EEG, and fMRI These techniques, while less direct than single-unit recordings, offer measures comparable to human neuroimaging data [11](#page=11). **Local field potentials (LFPs):** LFPs represent the summed synaptic potentials, afterpotentials, and membrane oscillations of nearby cells, but \_not action potentials of output neurons. They complement action potential recordings and correlate with signals obtained from non-invasive human neuroimaging methods like EEG and fMRI [11](#page=11). **Electroencephalography (EEG):** EEG records electrical activity from electrodes placed at a greater distance from neurons, sometimes intracranially in animal studies [11](#page=11). **Functional Magnetic Resonance Imaging (fMRI):** fMRI measures activity from a distance, relying on hemodynamic responses after neuronal activity, related to blood flow and oxygenation [11](#page=11). ##### 2.2.3.1 LFP and EEG Studies comparing LFP and EEG in monkeys and humans during tasks like visual attention and saccades show significant similarities in their signals. Because monkey EEG is known to correlate with spike rates, these similarities suggest that human EEG reflects similar neuronal activity [12](#page=12). ##### 2.2.3.2 LFP and fMRI Correlations have been observed between LFP recordings and fMRI signals in monkeys during visual stimulation tasks. By comparing flattened brain maps of monkeys and humans, similar activated regions are seen in fMRI studies. Since monkey fMRI signals are known to relate to LFPs, it is inferred that human fMRI signals reflect similar local field potentials. This provides crucial evidence for what fMRI measures in the brain, specifically hemodynamic adjustments following neuronal activity [12](#page=12). * * * # Neuroimaging techniques: EEG and fMRI This section delves into two prominent neuroimaging techniques, electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), detailing their underlying principles, data acquisition, analysis methods, and applications in understanding brain function. ### 3.1 Electroencephalography (EEG) EEG is a neuroimaging technique that measures electrical activity in the brain through electrodes placed on the scalp. It excels in temporal resolution, allowing for the instantaneous measurement of brain activity, but has limited spatial resolution because measurements are taken from the outside of the head, making it difficult to pinpoint the exact location of activity within the brain. EEG is completely non-invasive. The primary question answered by EEG is "when is something happening?" [25](#page=25). #### 3.1.1 History of EEG The history of EEG includes significant milestones: * **Richard Caton:** Recorded electrical impulses from rabbit and monkey brains, suggesting a link between electrical current in gray matter and its function [26](#page=26). * **Adolf Beck & Napoleon Cybulski:** Investigated spontaneous electrical brain activity, rhythmic oscillations, and cortical activity in response to stimulation [26](#page=26). * **Napoleon Cybulski & Sabina Jelenska-Macieszyna:** Recorded experimentally induced epileptic seizures from a dog's cortex [26](#page=26). * **Hans Berger:** First recorded electrical activity of the human brain from the scalp, establishing EEG as we know it [26](#page=26). * **William Grey Walter:** Introduced the contingent negative variation, an early form of Event-Related Potential (ERP) [26](#page=26). #### 3.1.2 Neural basis of EEG EEG reflects voltages generated by postsynaptic potentials from neocortical pyramidal cells [26](#page=26). * **Postsynaptic potentials (PSPs):** These are slow, graded potentials that arise in a neuron after being stimulated by another neuron's axon, affecting local ion concentrations. PSPs are crucial for EEG as they overlap and generate a measurable signal, unlike the rapid, all-or-none action potentials which are not well-detected by EEG [27](#page=27). * **Dipoles:** When considering a whole neuron, a dipole represents a difference in charge between two parts of the neuron. This separation of charge over a small distance is fundamental to measuring neural activity [27](#page=27). * **Summation:** For EEG to detect activity, synchronous dipoles from aligned neurons must summate. This summation can be modeled as a single equivalent dipole with parameters for location, orientation, and strength. Pyramidal neurons are ideal generators due to their spatial alignment and perpendicular orientation to the cortical surface [28](#page=28). * **Scalp distribution and EEG waveforms:** Electrode placement on the scalp measures changes in voltage over time, reflecting the summation of PSPs in cortical neurons. The signal strength and polarity depend on the dipole's orientation, electrode location, and distance from the dipole [28](#page=28). #### 3.1.3 What EEG can and cannot measure EEG can measure: * Synchronous activity of PSPs in thousands of pyramidal cells in the cortex [28](#page=28). * Neural dynamics of cognitive processes in real-time due to its excellent temporal resolution [28](#page=28). * Direct neural activity occurring in the brain [28](#page=28). EEG cannot measure: * Action potentials [28](#page=28). * Single neuronal events [28](#page=28). * Asynchronous neural activity [29](#page=29). * Dipole activity not organized in parallel, as signals will cancel out [29](#page=29). * Activity in subcortical and deep brain structures like the thalamus, basal ganglia, or brainstem [29](#page=29). #### 3.1.4 EEG data collection EEG data is collected using electrodes, which are metal discs forming an electrical connection with the scalp. The EEG signal is the voltage between an electrode and a reference electrode, typically placed in a neutral location like behind the ear. Electrode placement often follows standardized systems, such as the 10-20 system, to ensure comparability across studies. The setup involves an EEG cap connected to an amplifier and a computer, with synchronized markers indicating experimental events. The resulting data is a mix of cortical activity and noise from muscle movements, heartbeat, etc. [29](#page=29). #### 3.1.5 Data analysis in EEG EEG data analysis primarily focuses on extracting the relevant neural signal from the noise. ##### 3.1.5.1 ERP analyses Event-Related Potentials (ERPs) are used to isolate brain responses time-locked to specific events. * **Segmentation:** The EEG signal is divided into epochs, time-locked to the stimulus of interest, often including a baseline, stimulus, and response period [29](#page=29). * **Averaging:** Multiple epochs are averaged together. Random noise, uncorrelated with the stimulus, is averaged out, while the systematic, stimulus-related signal remains. This process reveals signals as small as 1 microvolt (μV) embedded in noise up to 100 μV. More trials are needed if there is more noise [29](#page=29). * **ERP Waveform:** This represents changes in scalp-recorded voltage over time, reflecting cognitive activity [30](#page=30). * **ERP Peak:** A reliable local positive or negative maximum in the ERP waveform, not due to noise. Negatives are plotted up, positives down. Amplitude differences can indicate attention [30](#page=30). * **ERP Component:** A scalp-recorded voltage change reflecting a neural or psychological process. Components are labeled by polarity (positive/negative) and latency (e.g., P100, N200). The P1 or P100 component, for example, is the first positive deflection around 100ms, indicative of fast sensory processing [30](#page=30). ##### 3.1.5.2 Visualizing ERPs * **Time-domain plot:** Shows how electrical potentials change over time at individual or averaged electrodes [30](#page=30). * **Topographical map:** Displays the distribution of electrical potential across the scalp at a specific time point or period [30](#page=30). ##### 3.1.5.3 Example: Mismatch Negativity (MMN) The MMN is a robust ERP component elicited by a deviant stimulus within a series of standard stimuli, demonstrating sensory memory modulation by training [30](#page=30). ##### 3.1.5.4 Clinical applications of ERPs ERPs are used in clinical settings to assess consciousness levels in coma patients and differentiate between vegetative and minimally conscious states. They provide insights into cognitive processing beyond observational capabilities [30](#page=30) [31](#page=31). ##### 3.1.5.5 (Time-)frequency analyses This analysis examines oscillations in the EEG signal, which emerge from synchronized fluctuations of postsynaptic potentials in large neuron groups [31](#page=31). * **Waves and Rhythms:** Different frequency bands are associated with various cognitive states, including Alpha (8-12Hz, visual processing), Beta (15-30Hz, active processing), Theta (4-8Hz, cognitive control), Delta (2-4Hz, deep relaxation), and Gamma (>30-80Hz or 80-150Hz, strong sensory stimulation). Boundaries between these bands are not strictly defined [31](#page=31). * **Wave Characteristics:** Frequency (speed), Power (amplitude/number of neurons), and Phase (position in the cycle) are key characteristics [31](#page=31). * **Time-locked vs. Phase-locked:** Signals can be time-locked to an event or phase-locked to the oscillation cycle. ERPs primarily capture time- and phase-locked signals, while time-frequency analysis can reveal signals that are only time-locked [31](#page=31). * **Fourier Transform:** A mathematical tool that represents any time series as a sum of sine waves, allowing for the breakdown of complex signals into their frequency components. This helps determine the "power" of different frequencies within the signal [32](#page=32). ##### 3.1.5.6 Discoveries from frequency analysis Frequency analysis has revealed important findings: * **Frequency:** The temporal resolution of perception can be determined by alpha oscillation frequency [33](#page=33). * **Phase:** The detection of faint stimuli is influenced by the phase of alpha oscillations [33](#page=33). * **Power:** Lower alpha wave power on the contralateral side indicates more desynchronization and increased attention to a stimulus [33](#page=33). ##### 3.1.5.7 Application of frequency analysis Time-frequency analysis is notably used in research of sleep stages to identify problems or irregularities [33](#page=33). #### 3.1.6 Statistical analyses in EEG EEG data analysis involves numerous statistical decisions, leading to the "garden of forking paths" problem. Researchers must select components, time windows, and regions of interest. To mitigate the risk of false positives arising from multiple comparisons, a priori hypotheses and pre-registration of analysis plans are crucial, along with corrections for multiple comparisons [34](#page=34). #### 3.1.7 Pros and cons of EEG **Pros:** * Inexpensive and non-invasive [34](#page=34). * Little effect on subjects' perception and behavior [34](#page=34). * High temporal resolution for assessing the dynamics of cognitive processes [34](#page=34). * Direct measure of neural activity [34](#page=34). * Multidimensional (time, space, frequency, power) [34](#page=34). **Cons:** * Low spatial resolution due to volume conduction and the inverse problem, making precise localization challenging [34](#page=34). * High number of degrees of freedom in analysis, leading to multiple comparison problems [34](#page=34). * Requires a strong theoretical a priori hypothesis [34](#page=34). #### 3.1.8 Magnetoencephalography (MEG) MEG is a technique that measures magnetic fields generated by neuronal sources in the brain. It offers excellent temporal resolution but requires specialized equipment like SQUIDs (superconducting quantum interference devices) and is sensitive to magnetic interference [34](#page=34). ### 3.2 Functional Magnetic Resonance Imaging (fMRI) Functional Magnetic Resonance Imaging (fMRI) has become a dominant measure of brain activity over the last two decades due to its safe, non-invasive nature, and a good balance between spatial and temporal resolution. It provides insights into the link between brain regions and behavior, such as perception and language [36](#page=36). #### 3.2.1 Historical context of brain function localization Early understanding of brain function localization came from studies of patients with brain lesions: * **M. Broca:** Identified Broca's area in the frontal lobe responsible for language production in a patient with expressive aphasia [36](#page=36). * **Phineas Gage:** A severe frontal lobe injury dramatically altered his personality and behavior, highlighting the role of the frontal lobe [36](#page=36). * **H.M.:** Removal of the hippocampus resulted in profound memory loss, linking this structure to memory consolidation [36](#page=36). #### 3.2.2 MRI: Functional and structural imaging Magnetic Resonance (MR) imaging uses magnetic fields and radio waves to create detailed images of organs and tissues. Both structural and functional MR images are acquired using the same fundamental technique, but different sequences emphasize different tissue characteristics [36](#page=36) [37](#page=37). * **Structural MRI:** Provides detailed images of brain anatomy and structure (e.g., T1-weighted images) [37](#page=37). * **Functional MRI (fMRI):** Uses T2\*-weighted images to capture changes in brain activity over time while a person performs a task. The functional activity is then superimposed on a structural image to visualize which brain regions are active [37](#page=37). #### 3.2.3 MRI scanners MRI scanners have three main components: a radio frequency coil, gradient coils, and a magnet. Hydrogen protons within water molecules possess magnetic properties that allow them to align with a strong magnetic field (B0) and re-emit signals when perturbed by radio waves, forming the basis of the MR signal. The static magnetic field, typically ranging from 1.5 to 7 Tesla, aligns these protons. Gradient coils enable spatial localization, allowing for slice-by-slice imaging. The radio frequency coil acts as an antenna for transmitting and detecting radio waves [37](#page=37) [38](#page=38) [39](#page=39). #### 3.2.4 Generating brain images Brain images can be generated with different contrasts by manipulating the repetition time (TR) and echo time (TE) of the radio frequency pulses, emphasizing different tissue characteristics like T1 and T2 values [39](#page=39). #### 3.2.5 Functional MRI: The BOLD signal fMRI detects changes in neural activation by relying on the difference in magnetic properties between oxygenated and deoxygenated blood, a phenomenon known as the Blood Oxygenation Level Dependent (BOLD) contrast [40](#page=40). * **Neurovascular Coupling:** Activated brain regions experience increased blood flow and oxygen supply, leading to a relative decrease in deoxygenated hemoglobin. Oxygenated hemoglobin is more "diamagnetic" and causes less magnetic distortion than paramagnetic deoxygenated hemoglobin. This change in the ratio of oxygenated to deoxygenated hemoglobin affects the T2\* decay process and results in a measurable change in the MRI signal [40](#page=40). * **Hemodynamic Response Function (HRF):** The BOLD signal change in response to neural activity is delayed and peaks approximately 4 to 6 seconds after the onset of the event. It involves an initial dip, a peak, and then a return to baseline, sometimes with an undershoot [41](#page=41). #### 3.2.6 Functional vs. anatomical images Functional images are acquired faster but are blurrier than high-resolution structural images. Typically, fMRI analysis combines both, overlaying functional activity onto structural anatomy for interpretation [37](#page=37) [42](#page=42). #### 3.2.7 fMRI data analysis fMRI data analysis follows a pipeline: 1. **Experimental Design:** Formulating a research question and hypothesis, and designing the experiment (block design or event-related design) to effectively test it. Block designs group similar events, offering statistical power but risking habituation. Event-related designs mix events to avoid order effects and habituation but have lower signal-to-noise ratios [42](#page=42) [43](#page=43). 2. **Data Acquisition:** This involves a localizer scan, an anatomical scan, and functional scans [46](#page=46). 3. **Preprocessing:** Cleaning the raw fMRI data, which includes steps like slice-time correction, head motion correction, co-registration of functional and structural images, normalization to a standard brain template (e.g., MNI or Talairach space), and spatial filtering (smoothing) (#page=46,47,48) [46](#page=46) [47](#page=47) [48](#page=48). 4. **Data Analysis:** This typically involves a first-level analysis (within-subject) and a second-level analysis (group-level) [48](#page=48). ##### 3.2.7.1 First-level analysis This analysis is performed at the individual subject level. * **Voxel:** The brain is divided into small cubic units called voxels (volumetric pixels), commonly around 3 mm³ [48](#page=48). * **Time Series:** The signal intensity within a single voxel across acquired volumes over time constitutes its time series [48](#page=48). * **General Linear Model (GLM):** This statistical framework is used to model the fMRI signal (Y) as a linear combination of predictor variables (X) representing the experimental design, plus an error term (ε). The GLM estimates beta weights (ß) that quantify the contribution of each predictor to the observed fMRI signal. The model includes predictors for experimental conditions of interest and potential confounds like head movement [49](#page=49) [50](#page=50). ##### 3.2.7.2 Second-level analysis This analysis aggregates results across multiple subjects to test the research hypothesis at the group level, often using Random Effects (RFX) analysis. Common tests include one-sample t-tests to assess group-level activation and two-sample t-tests to compare activation between different groups [52](#page=52). #### 3.2.8 The multiple comparison problem in fMRI Due to the large number of voxels analyzed (mass-univariate approach), performing thousands of statistical tests increases the risk of Type I errors (false positives). Statistical corrections like False Discovery Rate (FDR) or Family-Wise Error Rate (FWE) are necessary to control the cumulative alpha error [54](#page=54). #### 3.2.9 ROI analysis Region of Interest (ROI) analysis involves selecting specific clusters of voxels or brain regions for focused examination. This can reduce the number of statistical corrections needed and allows for functional specification of a region's role in different manipulations [54](#page=54). #### 3.2.10 Summary of fMRI **Pros:** * Excellent spatial resolution (in millimeters) [55](#page=55). * Harmless and non-invasive [55](#page=55). * Allows testing of precise hypotheses about neuro-cognitive architecture [55](#page=55). **Cons:** * Low temporal resolution (around 2 seconds), limited by physiological constraints [55](#page=55). * Measures hemodynamic changes, not direct neural effects [55](#page=55). * Expensive in terms of scanner, maintenance, and operation [55](#page=55). * The large number of voxels leads to the multiple comparison problem, necessitating a strong a priori hypothesis [55](#page=55). #### 3.2.11 Additional fMRI analyses * **Multivariate Pattern Analysis (MVPA):** Unlike univariate approaches that average activity across voxels, MVPA examines patterns of activity across many voxels simultaneously within a region. This can reveal condition-dependent differences missed by averaging and utilizes unsmoothed single-subject data to preserve fine-grained voxel patterns [56](#page=56). * * * # Brain stimulation techniques: TMS and TES Brain stimulation techniques, specifically Transcranial Magnetic Stimulation (TMS) and Transcranial Electrical Stimulation (TES), are non-invasive methods used to modulate neural activity for research and clinical applications [64](#page=64) [72](#page=72). ### 4.1 Transcranial Magnetic Stimulation (TMS) #### 4.1.1 Basics and principles TMS is a non-invasive technique that temporarily interferes with or modulates neural activity in a cortical region, impacting related cognitive functions. It operates on Faraday's principle of electromagnetism, where a rapidly changing current in a conductor (the coil) generates a magnetic field that, in turn, induces an electric field in a nearby conductor (the brain). This induced electric field can depolarize neurons, generating action potentials and interfering with ongoing neural activity. The primary goal of using TMS is to establish causal relationships between brain activity and cognitive functions by observing behavioral changes following stimulation or inhibition of specific cortical areas. Unlike neuroimaging techniques, TMS directly interferes with neural processing [64](#page=64). #### 4.1.2 Factors influencing TMS effects The effects of TMS are influenced by several factors: * **Location of the brain region:** TMS primarily affects the cortical surface (gyri) due to a limited stimulation depth of approximately 2 cm, with stimulation in sulci being more restricted. Subcortical areas can only be stimulated with non-standard coils [65](#page=65). * **Coil characteristics:** The shape, position, and orientation of the coil determine the focality and depth of stimulation. A figure-8 coil is commonly used for its precision and limited spread of activity to neighboring areas [65](#page=65). * **Experimental parameters:** This includes the intensity, frequency, and timing of stimulation [66](#page=66) [67](#page=67). * **Intensity:** Stimulation intensity is often defined as a percentage of a subject's motor threshold (MT) or phosphene threshold (PT), which are the lowest intensities required to evoke motor-evoked potentials (MEPs) or phosphenes in 50% of trials, respectively. Intensities above the threshold generate action potentials, while those below modulate the resting potential [66](#page=66). * **Frequency:** Different protocols exist, including single pulses (used for threshold determination), paired pulses (to study intra-cortical inhibition/facilitation), double pulses (to study inter-connectivity), and repetitive TMS (rTMS) at frequencies of 1-20+ Hz, which can have inhibitory (1 Hz) or excitatory (5-20 Hz) effects and induce long-term modulation of cortical excitability [66](#page=66). * **Timing:** Stimulation can be applied "on-line" during task performance, with effects lasting only during the task, or "off-line" before the task, leading to effects that persist beyond the stimulation period [67](#page=67). * **State of neurons:** The ongoing neural activity at the time of stimulation influences the TMS effect, a phenomenon related to stochastic resonance. Adding noise can improve processing of weak signals but disrupt it if the ongoing noise level is too high [67](#page=67). #### 4.1.3 Methods for targeting brain regions Accurate targeting of brain regions for TMS is achieved through several methods: * **Functional localization:** Stimulating an area and observing an immediate effect to identify its role (e.g., stimulating the primary motor cortex (M1) to elicit MEPs in the hand) [65](#page=65). * **Anatomical landmarks:** Using descriptive anatomical references to guide coil placement [65](#page=65). * **10-20 international EEG system:** Employing an EEG cap to locate specific brain regions corresponding to electrode positions (e.g., Broca's area under F3) [65](#page=65). * **Neuronavigation:** Using 3D brain reconstructions to input coordinates and precisely guide stimulation to the target location [65](#page=65). It's important to consider both primary and secondary effects, as TMS often influences neighboring and directly connected regions [65](#page=65). #### 4.1.4 Experimental designs and applications * **Motor-evoked potentials (MEPs):** Applying TMS to the primary motor cortex elicits MEPs in the contralateral hand, with the amplitude serving as an index of corticospinal excitability. MEP amplitude increases when a participant prepares to move, allowing for the study of motor monitoring and imitation tendencies [69](#page=69). * **fMRI-guided TMS:** Combining fMRI to identify peak activity within subjects with neuronavigation allows for precise TMS application to specific regions identified by fMRI, enabling a "virtual lesion" approach to study the role of these areas [69](#page=69). #### 4.1.5 Clinical applications TMS has experimental applications in motor, visual, language, and memory systems, and for studying corticocortical connections. Clinically, it is used therapeutically for conditions such as stroke rehabilitation, tinnitus, anxiety disorders, major depressive disorder (targeting DLPFC with high or low frequency rTMS), migraines, and movement disorders. The mechanism of clinical application involves altering cortical excitability, either directly in the dysfunctional area or indirectly in upstream/downstream areas, potentially leading to long-term changes in synaptic plasticity [70](#page=70). #### 4.1.6 Safety Strict safety checklists are crucial for TMS to exclude individuals with contraindications like high blood pressure, history of seizures, or pregnancy [70](#page=70). #### 4.1.7 Pros and cons **Pros:** * Non-invasive [71](#page=71). * Allows for causal interference [71](#page=71). * Good temporal (milliseconds) and spatial resolution (depending on parameters) [71](#page=71). * No long-term side effects [71](#page=71). * Flexible for within- and between-subjects designs [71](#page=71). * Clinical applications [71](#page=71). **Cons:** * Shallow stimulation depth (approximately 2 cm) [71](#page=71). * Requires a good control condition (group/sham) [71](#page=71). * Strict exclusion criteria for safety [71](#page=71). * Can be painful or annoying due to muscular twitches [71](#page=71). ### 4.2 Transcranial Electrical Stimulation (TES) #### 4.2.1 Basics and principles TES, commonly referred to as Transcranial Direct Current Stimulation (tDCS), is a technique involving a stimulator and two electrodes (an anode and a cathode) to deliver a constant electrical current of 1-2 milliamperes (mA). The current flows from the positive pole (anode) to the negative pole (cathode), influencing neuronal excitability through polarity-dependent modulation [72](#page=72). * **Anodal tDCS:** Applied under the anode, it increases cortical excitability by depolarizing the resting membrane potential, leading to more spontaneous neuronal firing [72](#page=72). * **Cathodal tDCS:** Applied under the cathode, it decreases cortical excitability by hyperpolarizing neurons, resulting in less spontaneous firing [72](#page=72). TES does not induce action potentials but modulates the resting membrane potential, increasing the probability of neuronal firing. Evidence from MEP modulation suggests that these effects are polarity-dependent and typically disappear after about 10 minutes [72](#page=72). #### 4.2.2 Conventional montage and electrode placement TES can be applied using various montages: * **Extracephalic reference:** One electrode on the target area and another outside the head [73](#page=73). * **Cephalic reference:** Typically involves the supraorbital area [73](#page=73). * **Bipolar montage:** Two active electrodes, where placement is crucial as the current flows from anode to cathode [73](#page=73). Electrodes are often sponges soaked in saline solution to improve conductivity. The size of the electrode influences the area and strength of modulation: larger sponges modulate a wider area but with weaker effects, while smaller sponges offer more precision and stronger effects [73](#page=73). #### 4.2.3 Timing Stimulation can be applied off-line (before a task), on-line (during a task), or a combination of both [73](#page=73). #### 4.2.4 Related methods Beyond conventional tDCS, alternative TES methods exist: * **tACS (Transcranial Alternating Current Stimulation):** The current alternates at a specific frequency, which can be manipulated along with amplitude [74](#page=74). * **otDCS (Oscillatory Direct Current Stimulation):** An oscillatory current is coupled with a direct current, maintaining polarity while manipulating current strength, frequency, and amplitude [74](#page=74). #### 4.2.5 Clinical use TES, particularly tDCS, has shown effectiveness in treating depression, often comparable to antidepressant medications after a few trials. It is also used for chronic and acute pain, stroke rehabilitation, and drug addiction [74](#page=74). #### 4.2.6 Experimental example (tDCS) Anodal tDCS applied to the temporoparietal junction for 20 minutes has been investigated for its potential to enhance social abilities, with studies showing improved accuracy during tasks [75](#page=75). #### 4.2.7 Controversies and limitations A review of 59 studies found no reliable evidence of tDCS effects on cognition in healthy populations, highlighting issues with reproducibility. The most reliable effect observed is MEP amplitude modulation, indicating increased motor cortex excitability [75](#page=75). #### 4.2.8 Experimental example (tACS) tACS can be used to manipulate rhythmic brain activity, which is important for stimulus processing. In an experiment, participants watched a flickering light, and tACS was applied to manipulate the synchrony between the flicker frequency and the tACS stimulation. The highest oscillation amplitude was observed when synchronized with tACS, providing evidence that tACS can modulate brain activity [75](#page=75). #### 4.2.9 Safety Safety considerations for tDCS include age restrictions (18+), avoiding use with metals in the head (except dental fillings), a history of seizures (personal or family), medications that lower seizure threshold, serious medical/neurological/psychiatric conditions, and pregnancy. Potential side effects include headaches, neck aches, hearing threshold shifts, syncope, and scalp/head metal presence [75](#page=75). #### 4.2.10 Pros and cons **Pros:** * Easy to apply [75](#page=75). * Inexpensive [75](#page=75). * Easy sham condition [75](#page=75). * Fewer "adverse" effects compared to some other techniques [75](#page=75). * * * # Neurotransmitter-based methods and their application Neurotransmitter-based methods allow for the direct investigation of chemical processes within the brain, providing insights into neurotransmitter systems that are crucial for understanding brain function and dysfunction [77](#page=77). ### 5.1 Understanding neurotransmitter systems Neurotransmission involves the release of neurotransmitters into the synaptic cleft, which then bind to receptors on the postsynaptic neuron, initiating or inhibiting action potentials. The type of neurotransmitter released is cell-type dependent [76](#page=76). Key neurotransmitters and their roles include: * **Monoamines:** * **Dopamine (DA):** Involved in motivation, learning, cognitive control, memory formation, and motor control. Major pathways include mesolimbic/mesocortical, nigrostriatal, and tuberoinfundibular projections [76](#page=76). * **Noradrenaline (NE):** Crucial for arousal, vigilance, attention, and memory formation, with widespread projections throughout the brain [76](#page=76). * **Serotonin (5-HT):** Associated with mood, emotion processing, and impulsivity, also having widespread projections [76](#page=76). * **Amino acids:** * **Excitatory:** Acetylcholine (ACh), Glutamate, Aspartate [76](#page=76). * **Inhibitory:** Gamma-aminobutyric acid (GABA), Glycine [76](#page=76). > **Tip:** While fMRI measures hemodynamic activity and EEG/single-cell recordings measure electrophysiological activity, neurotransmitter-based methods focus on the chemical level of brain activity [77](#page=77). ### 5.2 Signals measured by neurotransmitter-based methods These methods can investigate neurotransmitter activity at several levels: * **Receptor binding:** Measuring the binding of neurotransmitters to postsynaptic receptors, for example, dopamine D2 receptor PET scans [77](#page=77). * **Transporter binding:** Assessing the binding of transporter units on the presynaptic membrane, such as with dopamine transporter PET scans [77](#page=77). * **Metabolic spectrum:** Providing a snapshot of molecules within a specific brain region, exemplified by GABA MRS [77](#page=77). Although neurotransmitter-based methods generally have lower temporal and spatial resolution compared to other neuroimaging techniques, they offer unique insights into chemical and synaptic processes, particularly valuable in clinical research [77](#page=77). ### 5.3 Positron Emission Tomography (PET) Transmitter-specific PET utilizes radio-labeled ligands, known as tracers, most commonly incorporating isotopes like Carbon (C) and Fluorine (F) [77](#page=77). **Procedure Overview:** 1. A tracer is injected into the subject. 2. The radioisotope in the tracer decays within the tissue, emitting a positron [77](#page=77). 3. This positron collides with an electron, producing two gamma-ray photons [77](#page=77). 4. The PET camera detects and localizes these photon pairs, indicating the site of a binding event, such as receptor binding [77](#page=77). 5. A 3D image is constructed based on the accumulation of these detected events [77](#page=77). Tracer binding potential maps represent the inverse of the actual neurotransmitter binding. Higher tracer binding indicates that fewer endogenous neurotransmitters can bind to their target receptors, as the receptors are occupied by the tracer [77](#page=77). > **Note:** Non-transmitter-specific PET, which assesses cerebral blood flow (CBF) and glucose metabolism (FDG), has largely been superseded by fMRI due to fMRI's superior spatial and temporal resolution [77](#page=77). ### 5.4 Magnetic Resonance Spectroscopy (MRS) MRS is an MR-based technique used to determine the regional metabolic spectrum of brain tissue. It shares principles with MRI, involving the application of radio-frequency waves to nuclei spins within a magnetic field. However, unlike MRI which primarily focuses on the spin of hydrogen (H) in water molecules, MRS measures the resonance of other molecules [78](#page=78). The resulting spectrum displays the resonance of metabolites, measured in parts per million (ppm), which are associated with specific neurotransmitters or other brain substances [78](#page=78). **Key MRS Metabolites:** * **N-Acetyl Aspartate (NAA):** An indicator of neuron and axon integrity; a decrease suggests tissue loss or damage [78](#page=78). * **Choline (Cho):** Related to membrane turnover [78](#page=78). * **Creatine (Cre):** Associated with energy metabolism [78](#page=78). * **Glutamate (Glu):** An excitatory neurotransmitter [78](#page=78). * **GABA:** An inhibitory neurotransmitter [78](#page=78). **Applications:** MRS is frequently employed in clinical settings for conditions like tumors, Alzheimer's, and Parkinson's disease, particularly for analyzing neurotransmitter metabolites like glutamate and GABA. It can also be relevant in cognitive neuroscience to serve as a covariate for behavioral, fMRI, or EEG data [78](#page=78). > **Example:** In a study on automatic motor control, MRS was used to measure GABA levels in the Supplementary Motor Area (SMA) to investigate its role in inhibiting automatic responses triggered by subliminal primes. Higher GABA levels in the SMA were correlated with smaller priming effects, suggesting a role for GABA in counteracting automatic responses. Selecting a specific Region of Interest (ROI) is crucial, and fMRI can be used to define this ROI for MRS analysis [79](#page=79). ### 5.5 Pharmacological manipulations Neurotransmitter agonists, antagonists, and modulators are primarily used in the development and validation of treatments for neuropsychological disorders [79](#page=79). * **Agonists:** Bind to receptors and trigger similar downstream signaling cascades as the natural neurotransmitter, potentially with increased strength [79](#page=79). * **Antagonists:** Bind to receptors and block them, preventing other molecules from binding and thus inhibiting the natural signaling pathway [79](#page=79). In basic psychology research, mild manipulations, such as low-dose drugs or dietary changes (e.g., tryptophan depletion), are used to explore the relationship between neurotransmission and cognitive functions or personality [79](#page=79). > **Example:** Consuming certain molecules through diet can alter brain metabolism. A high tryptophan diet can increase serotonin creation by crossing the blood-brain barrier [80](#page=80). #### 5.5.1 Example 1: Dopamine receptor binding of different antipsychotic drugs PET or SPECT studies can investigate striatal dopamine receptor binding in patients with schizophrenia and healthy controls. In healthy volunteers, the tracer competes only with endogenous dopamine. In patients treated with dopamine antagonists, tracer binding is weaker because the receptors are blocked by the medication. Risperidone showed the strongest effect on striatal dopamine binding, indicating nearly complete blockade, while clozapine reduced schizophrenia symptoms with relatively normal dopamine transmission in the basal ganglia. PET generally offers better spatial resolution than SPECT [80](#page=80). #### 5.5.2 Example 2: Effects of serotonin on motivated behaviour A reward anticipation task with varying feedback probabilities was used to compare participants with normal serotonin levels to those with depleted serotonin levels (via dietary tryptophan depletion). Results indicated that with normal serotonin, high reward probability led to faster but less accurate responses. Serotonin depletion resulted in slower and more accurate responses, reflecting reduced impulsivity. This is relevant to depression, where initial antidepressant treatment can increase impulsivity before mood improvements manifest, due to transiently increased serotonin levels [81](#page=81). ### 5.6 Conclusion **Benefits:** * These methods directly measure neurotransmission, unlike fMRI which relies on indirect hemodynamic measures [81](#page=81). * They are particularly valuable for studying clinical conditions linked to neurotransmitter disturbances [81](#page=81). * Combining neurotransmitter-based methods with other neuroimaging techniques can reveal intricate relationships between neurotransmission, cognition, and behavior [81](#page=81). **Limitations:** * Generally exhibit low temporal resolution, often providing only a "snapshot" of the brain's state. While block designs are possible with PET [81](#page=81). * Possess lower spatial resolution compared to fMRI, often limiting analysis to specific regions [81](#page=81). * Access to PET scanners is limited, and the short half-life of some tracers necessitates on-site production (e.g., using a cyclotron) [81](#page=81). * Except for MRS, these methods are more invasive than fMRI, EEG, or TMS, involving radiation exposure, injections, or medication, and require medical supervision [81](#page=81). * * * # Advanced analytical techniques and critical perspectives in neuroscience This section delves into advanced neuroimaging analysis techniques, such as Multivariate Pattern Analysis (MVPA) and functional connectivity, alongside critical perspectives on the interpretation and reporting of neuroscience research. ### 6.1 Advanced analytical techniques #### 6.1.1 Multivariate pattern analysis (MVPA) MVPA is an advanced analytical technique that focuses on patterns of brain activity across multiple voxels simultaneously, rather than examining individual voxel activations one by one. This approach contrasts with traditional univariate analyses, which average activity across voxels within a region, potentially missing condition-dependent differences in voxel patterns [56](#page=56). * **Voxel pattern:** Refers to the pattern of activation levels across multiple voxels within a specific brain region [56](#page=56). * **Concept:** MVPA compares voxel patterns within a region across two independent datasets acquired with the same paradigm. For instance, it can differentiate between patterns of brain activity when viewing images of mice versus bananas [56](#page=56). * **Data Preparation:** MVPA utilizes unsmoothed, single-subject data, as smoothing can blur voxel patterns [56](#page=56). ##### 6.1.1.1 Representational similarity analysis (RSA) RSA is a method within MVPA that detects pattern dissimilarities by correlating condition-specific voxel patterns between two datasets. If the correlation is higher for "same stimuli" compared to "different stimuli," it suggests the region is sensitive to the stimulus category manipulation [57](#page=57). > **Example:** If stimuli differ on shape envelope (round vs. spiky) and shape features (smooth vs. textured), a higher correlation would be observed when all features are the same [57](#page=57). ##### 6.1.1.2 Decoding MVPA Decoding in MVPA aims to extract information about mental states from complex, multivariate brain activity patterns. This is achieved by training a classifier on one dataset and testing its performance on another, using the same stimuli [57](#page=57). * **Methods:** Linear and non-linear classifiers, such as Support Vector Machines, Fisher's Linear Discriminant, and Minimum Distance, can be used [57](#page=57). * **Cross-validation:** This technique involves multiple rounds of training and testing to increase the validity of activity prediction. A common method is 'leave-one-out', where all runs except one are used for training, and the classifier is tested on the held-out run [57](#page=57). #### 6.1.2 Functional connectivity analyses Functional connectivity analyses examine the temporal correlations between the activity of different brain regions [58](#page=58). ##### 6.1.2.1 Resting-state connectivity This is the oldest type of functional connectivity analysis and involves correlating spontaneous signal fluctuations during rest (awake but passive state). It provides a baseline measure of brain connectivity and is particularly useful for clinical groups who may have cognitive performance deficits, as it is design-free. Resting-state activity can reveal functionally linked networks, such as the default-mode network, and can be compared between groups or related to task-based activity and performance [58](#page=58) [59](#page=59). > **Tip:** Resting-state connectivity is design-free, making it suitable for clinical populations. However, it has no direct relation to task manipulation, though combined paradigms can be used [60](#page=60). ##### 6.1.2.2 Psycho-physiological interaction (PPI) PPI assesses the covariation of neural activity within the context of a cognitive task, examining how the psychological context (e.g., condition A vs. B) changes the connectivity pattern between regions. It tests whether this psychological context modulates connectivity between a seed region and other brain areas [59](#page=59). * **Seed region:** A specific brain region chosen as a starting point for the analysis [59](#page=59). * **PPI interaction term:** Corresponds to differences in regression slopes for different psychological contexts, accounting for effects beyond standard GLM results [59](#page=59). > **Example:** A study on attention to motion showed that connectivity between visual cortex regions (V1 and V5) was higher under an attention condition compared to a non-attention condition [59](#page=59). ##### 6.1.2.3 Effective connectivity: Dynamic Causal Modeling (DCM) DCM models the brain as a dynamic system of interconnected neural nodes, treating experimental conditions as perturbations of the system's dynamics. It involves fitting a set of a priori defined models to the data and comparing model fits to identify the best model describing the network structure [60](#page=60). > **Example:** A DCM study on motivation and rewards examined connections between the dorsolateral prefrontal cortex (DLPFC), ventral tegmental area (VTA), and nucleus accumbens (NAcc), considering driving inputs from high vs. low reward cues and modulatory inputs from high rewards only [60](#page=60). > **Tip:** DCM allows for model-based conclusions regarding directionality and causation but requires good models based on anatomy and function [60](#page=60). #### 6.1.3 Other related methods * **Diffusion Tensor Imaging (DTI):** This technique images white matter tracts and reflects actual structural connectivity between regions by tracking the diffusion of water molecules along axons. Structural integrity from DTI can be related to fMRI and performance data to reveal functional relevance [61](#page=61). > **Example:** Lower white matter values in the corpus callosum correlated with faster responses in rewarded trials, suggesting increased susceptibility to reward-based information [61](#page=61). * **Voxel-Based Morphometry (VBM):** VBM quantifies tissue volume by normalizing and segmenting individual structural MRI images. It can reveal differences in tissue volume between subjects or groups, and these anatomical differences can be related to fMRI data and cognitive tasks to infer functional relevance [61](#page=61). > **Example:** Fronto-temporal atrophy was observed in dementia groups, with the degree of atrophy predicting cognitive deficits [62](#page=62). * **Functional Near-Infrared Spectroscopy (fNIRS):** fNIRS uses near-infrared light to measure blood oxygenation and flow in superficial brain regions. It offers better temporal resolution than fMRI but is limited to the brain surface [62](#page=62). * **Functional Transcranial Doppler (fTCD):** fTCD uses ultrasound to measure blood flow velocity in the middle cerebral arteries, assessing functional asymmetry between hemispheres during cognitive tasks. It has good temporal resolution but poor spatial resolution [62](#page=62). ### 6.2 Critical perspectives in neuroscience #### 6.2.1 General issues ##### 6.2.1.1 Neuroenchantment Neuroenchantment refers to the tendency to overvalue or give more credence to findings when neuroscience is involved, even if the evidence is weak or misrepresented [93](#page=93). > **Example:** Studies found that students were more easily convinced of findings if presented with brain images, even if the imaging device was fake. However, these findings have been challenged, and awareness is crucial, especially when communicating with the public [93](#page=93). ##### 6.2.1.2 Blobology Blobology describes the practice of highlighting fMRI "blobs" (clusters of activated voxels) and attributing them to a single, isolated function. This is criticized because complex cognitive functions are rarely attributable to a single brain region, and many regions can be involved in various processes [93](#page=93). > **Example:** Claiming a specific brain blob is solely responsible for "love" is an oversimplification, as love is a complex construct involving multiple brain areas and processes [93](#page=93). ##### 6.2.1.3 Reverse inference Reverse inference is the practice of inferring a psychological construct based on the activation of a specific brain region, assuming that region is uniquely associated with that construct. This is problematic because most brain regions are involved in multiple cognitive functions [94](#page=94). > **Tip:** To avoid these pitfalls, rely on converging evidence from multiple studies, consider the baseline or comparison condition, and look at system-level operations rather than single-region activations. Be critical of how activations relate to observed behavior and consult experts [94](#page=94). #### 6.2.2 Statistical issues Neuroscience research often involves a high number of statistical comparisons due to the large number of voxels and complex analyses, leading to potential biases and false positives. ##### 6.2.2.1 Multiple comparisons problem When performing numerous statistical tests, the probability of obtaining false positive results increases significantly. Standard statistical thresholds (e.g., p < 0.001) may not be sufficient, especially for small cluster sizes [95](#page=95). > **Example:** A study on a non-living salmon showed significant brain activity clusters in response to emotional images, highlighting the risk of false positives from multiple comparisons [95](#page=95). **Solutions:** Algorithms like False Discovery Rate (FDR) and Family-Wise Error (FWE) in SPM, or 3dClustSim, are used to define significance thresholds. Bonferroni correction can be too conservative, and defining ROIs to correct for the number of ROIs is another option. This problem affects various methods involving multiple tests, not just fMRI [95](#page=95). ##### 6.2.2.2 Circular analysis ("double dipping") Circular analysis occurs when a region of interest (ROI) is defined based on the same data used to test a hypothesis within that ROI. This creates a bias because the decision to include the ROI was influenced by the data itself [95](#page=95). > **Example 1:** Defining an ROI based on a significant difference (A>B) and then testing the same difference (A-B) within that ROI leads to inflated statistical power due to non-independent regressors [95](#page=95). **Example 2:** Defining an ROI based on A>B and then testing A>C within that ROI biases the analysis in favor of condition A [95](#page=95). **Solutions:** Use ROIs defined independently from the literature, anatomical definitions, or functional localizers. Alternatively, employ a "leave-one-out" cross-validation approach to create independent ROIs for each subject. A significant proportion of fMRI papers were found to contain circular analyses, leading to invalid inferences, though practices have improved since [95](#page=95). ##### 6.2.2.3 "Voodoo correlations" This refers to fMRI studies reporting correlations with personality data that are unrealistically high (r > 0.8), exceeding the upper bounds set by the reliability of the measures. This inflation is often due to non-independent analyses, such as averaging activity from only the most significant voxels [96](#page=96). > **Solution:** Extract fMRI activity from a data-independent ROI and correlate the mean activity of all voxels within that ROI with the measure of interest [96](#page=96). ##### 6.2.2.4 Related issues: p-hacking, excess success, publication bias * **p-hacking:** The practice of selectively collecting, analyzing, or reporting data until a statistically significant result is achieved. This can involve excluding participants, trials, or covariates without a priori justification [96](#page=96). * **Excess success:** Reported findings are often "too good to be true," with effect sizes exceeding what is expected given the sample size and power estimates. This can result from suppressing parts of findings or using selective analyses [96](#page=96). * **Publication bias:** Studies with significant results are more likely to be published than those with null results, creating a bias towards findings that align with existing literature and potentially obscuring the full picture of research outcomes [96](#page=96). > **Conclusion:** These issues collectively lead to a skewed perception of neuroscience research results. Increasing transparency through data/code sharing, preregistration, and publishing null results is crucial for improving research integrity. Journals and reviewers should also be more critical of statistical reporting and overly successful replications [96](#page=96). * * * ## Common mistakes to avoid * Review all topics thoroughly before exams * Pay attention to formulas and key definitions * Practice with examples provided in each section * Don't memorize without understanding the underlying concepts

| Term | Definition | |------|------------| | Skin Conductance | A measure of the electrical conductivity of the skin, reflecting sympathetic nervous system activity and arousal levels. | | Electro-dermal Activity (EDA) | The electrical activity of the skin, primarily measured as skin conductance or skin potential, which is influenced by sweat gland activity. | | Tonic Measure | A physiological measure that reflects a slow-changing, baseline level of activity over time, such as Skin Conductance Level (SCL). | | Phasic Response | A rapid, transient change in a physiological measure, often in response to a specific stimulus or event, such as Non-specific Skin Conductance Response (NS-SCR) or Event-Related Skin Conductance Response (ER-SCR). | | Pupillometry | The measurement of pupil size, which can reflect changes in arousal, mental effort, and autonomic nervous system activity, independent of light levels. | | Locus Coeruleus | A region in the brainstem that is a principal site for the synthesis of norepinephrine, a neurotransmitter implicated in arousal, attention, and stress responses, and which projects to various brain areas, including influencing pupil dilation. | | Electrocardiography (ECG) | A non-invasive technique that records the electrical activity of the heart, used to measure heart rate (HR) and heart rate variability (HRV). | | Heart Rate Variability (HRV) | The variation in the time intervals between consecutive heartbeats, reflecting the influence of the autonomic nervous system on heart regulation. | | Impedance Cardiography (ICG) | A non-invasive method that measures changes in the electrical impedance of the thorax to assess cardiac output and related parameters like the pre-ejection period. | | Pre-Ejection Period (PEP) | The time interval from the electrical stimulation of the ventricles to the opening of the aortic valve, thought to reflect sympathetic nervous system influence on cardiac contractility. | | Electromyogram (EMG) | A technique that measures the electrical activity produced by skeletal muscles, typically by placing electrodes on the skin over the muscle. | | Blink Reflex | An involuntary eye blink triggered by a stimulus, often used to study startle responses and the integration of autonomic and somatic nervous systems. | | Neuron | A nerve cell; the fundamental unit of the nervous system, responsible for transmitting information through electrical and chemical signals. | | Action Potential | A brief, all-or-none electrical depolarization that travels along the axon of a neuron, transmitting a nerve impulse. | | Synapse | The junction between two neurons, or between a neuron and a target cell, where information is transmitted, typically via neurotransmitters. | | Neurotransmitter | A chemical messenger released from a neuron that transmits a signal across a synapse to a target cell, influencing its activity. | | Local Field Potential (LFP) | A measure of the summed extracellular electrical activity of neuronal populations, reflecting synaptic activity and membrane potentials rather than individual action potentials. | | Electroencephalography (EEG) | A non-invasive neurophysiological monitoring method that records the electrical activity of the brain through electrodes placed on the scalp. | | Functional Magnetic Resonance Imaging (fMRI) | A neuroimaging technique that measures brain activity by detecting changes in blood oxygenation (BOLD signal), providing spatial information about brain function. | | Transcranial Magnetic Stimulation (TMS) | A non-invasive brain stimulation technique that uses magnetic pulses to induce electrical currents in the brain, modulating neural activity in targeted cortical regions. | | Transcranial Electrical Stimulation (TES) | A group of non-invasive brain stimulation techniques, including transcranial direct current stimulation (tDCS) and transcranial alternating current stimulation (tACS), that use electrical currents applied to the scalp to modulate neuronal excitability. | | Neurotransmitter-based Methods | Techniques used to study the role and levels of specific neurotransmitters in the brain, such as Positron Emission Tomography (PET) and Magnetic Resonance Spectroscopy (MRS). | | Positron Emission Tomography (PET) | An imaging technique that uses radioactive tracers to measure metabolic processes, neurotransmitter binding, or blood flow in the brain, providing insights into neurochemistry. | | Magnetic Resonance Spectroscopy (MRS) | An MRI-based technique used to measure the concentration of specific metabolites within a region of the brain, providing information about tissue chemistry and neuronal integrity. | | MVPA (Multivariate Pattern Analysis) | An analytical approach that examines patterns of brain activity across multiple voxels simultaneously to decode cognitive states or stimuli, rather than focusing on single-voxel activation. | | RSA (Representational Similarity Analysis) | An analytical method used to compare patterns of brain activity across different experimental conditions or subjects, often by calculating correlations between voxel patterns to understand representational similarity. | | Functional Connectivity | The statistical correlation or temporal coupling of neural activity between different brain regions, indicating how they interact or work together. | | Psychophysiological Interaction (PPI) | An fMRI analysis technique used to investigate how the connectivity between two brain regions changes as a function of psychological context or experimental conditions. | | DCM (Dynamic Causal Modelling) | A model-based approach used to infer directed and causal influences between brain regions, analyzing how experimental manipulations affect the effective connectivity within a network. | | DTI (Diffusion Tensor Imaging) | An MRI technique that measures the diffusion of water molecules in brain tissue to infer the structural connectivity of white matter tracts. | | VBM (Voxel-Based Morphometry) | An MRI analysis technique that quantifies differences in brain tissue volume or density across subjects or groups by comparing Voxel-wise measurements after normalization. | | Neuroenchantment | The tendency to be overly impressed by neuroscience findings, often attributing undue importance or explanatory power to brain-related information. | | Blobology | The practice of oversimplifying fMRI findings by attributing complex cognitive functions to localized 'blobs' of activation without considering network interactions or reverse inference. | | Reverse Inference | The logical fallacy of inferring the presence of a specific cognitive process from the activation of a particular brain region, when that region may be involved in multiple processes. | | Multiple Comparisons Problem | The statistical challenge that arises when performing numerous statistical tests, increasing the probability of obtaining false positive results due to chance. | | Circular Analysis | An analytical approach where the same data are used both to define a region of interest and to test a hypothesis within that region, leading to inflated results and invalid inferences. | | Voodoo Correlations | Unusually high correlations reported between fMRI activity and behavioral or personality measures, often resulting from methodological issues like circular analysis or p-hacking. | | p-hacking | The practice of conducting numerous statistical analyses on a dataset and selectively reporting only those that yield statistically significant results. | | Excess Success | The phenomenon where research findings, particularly in neuroscience, appear too good to be true, often due to selective reporting, publication bias, or flawed analyses. | | Publication Bias | The tendency for studies with positive or significant results to be more likely to be published than studies with null or negative results, potentially skewing the scientific literature. | | Clinical Between-Group Design | An experimental design where participants are divided into groups based on a clinical characteristic (e.g., diagnosis, treatment status) to compare their performance or neural activity. | | Within-Subjects Design | An experimental design where each participant is exposed to all experimental conditions or manipulations, allowing for direct comparison of their responses. | | Lesion Studies | Research that examines the behavioral or cognitive consequences of brain damage to infer the function of the affected brain areas. | | ECoG (Electrocorticography) | A method of recording brain activity directly from the surface of the brain using electrodes placed on the dura mater or cerebral cortex. | | SEEG (Stereoelectroencephalography) | A method of recording brain activity using depth electrodes surgically implanted into specific brain structures. | | DBS (Deep Brain Stimulation) | A neurosurgical procedure involving the implantation of electrodes in specific brain areas to deliver electrical impulses, used to treat various neurological and psychiatric disorders. | | VNS (Vagus Nerve Stimulation) | A treatment that involves implanting a device to stimulate the vagus nerve, used primarily for epilepsy and depression, believed to modulate neurotransmission. | | Neurotransmission | The process by which neurons communicate with each other via chemical signals (neurotransmitters) across synapses. | | Agonist | A substance that binds to a receptor and activates it, mimicking the effect of the natural neurotransmitter. | | Antagonist | A substance that binds to a receptor and blocks its activation by the natural neurotransmitter or agonists. | | Modulator | A substance that alters the activity of a receptor or neurotransmitter system without directly activating or blocking it. | | PET (Positron Emission Tomography) | An imaging technique that uses radioactive tracers to measure metabolic processes, neurotransmitter binding, or blood flow in the brain, providing insights into neurochemistry. | | MRS (Magnetic Resonance Spectroscopy) | An MRI-based technique used to measure the concentration of specific metabolites within a region of the brain, providing information about tissue chemistry and neuronal integrity. | | Functional Connectivity | The statistical correlation or temporal coupling of neural activity between different brain regions, indicating how they interact or work together. | | Resting-State Connectivity | The correlation of spontaneous brain activity between different brain regions when an individual is not engaged in a specific task, often reflecting intrinsic functional networks. | | Psycho-Physiological Interaction (PPI) | An fMRI analysis technique used to investigate how the connectivity between two brain regions changes as a function of psychological context or experimental conditions. | | DCM (Dynamic Causal Modelling) | A model-based approach used to infer directed and causal influences between brain regions, analyzing how experimental manipulations affect the effective connectivity within a network. | | DTI (Diffusion Tensor Imaging) | An MRI technique that measures the diffusion of water molecules in brain tissue to infer the structural connectivity of white matter tracts. | | VBM (Voxel-Based Morphometry) | An MRI analysis technique that quantifies differences in brain tissue volume or density across subjects or groups by comparing Voxel-wise measurements after normalization. | | fNIRS (functional Near-Infrared Spectroscopy) | A non-invasive neuroimaging technique that uses near-infrared light to measure changes in blood oxygenation and blood flow in the brain's superficial layers. | | fTCD (functional Transcranial Doppler) | A non-invasive neuroimaging technique that uses ultrasound to measure blood flow velocity in major cerebral arteries, providing insights into cerebral hemodynamics and lateralization. | | Resting-State Connectivity | The correlation of spontaneous brain activity between different brain regions when an individual is not engaged in a specific task, often reflecting intrinsic functional networks. | | Psychophysiological Interaction (PPI) | An fMRI analysis technique used to investigate how the connectivity between two brain regions changes as a function of psychological context or experimental conditions. | | DCM (Dynamic Causal Modelling) | A model-based approach used to infer directed and causal influences between brain regions, analyzing how experimental manipulations affect the effective connectivity within a network. | | DTI (Diffusion Tensor Imaging) | An MRI technique that measures the diffusion of water molecules in brain tissue to infer the structural connectivity of white matter tracts. | | VBM (Voxel-Based Morphometry) | An MRI analysis technique that quantifies differences in brain tissue volume or density across subjects or groups by comparing Voxel-wise measurements after normalization. | | fNIRS (functional Near-Infrared Spectroscopy) | A non-invasive neuroimaging technique that uses near-infrared light to measure changes in blood oxygenation and blood flow in the brain's superficial layers. | | fTCD (functional Transcranial Doppler) | A non-invasive neuroimaging technique that uses ultrasound to measure blood flow velocity in major cerebral arteries, providing insights into cerebral hemodynamics and lateralization. | | Neurotransmitter | A chemical messenger released from a neuron that transmits a signal across a synapse to a target cell, influencing its activity. | | Receptor Binding | The process by which a neurotransmitter or drug molecule binds to a specific receptor protein on a cell surface, initiating a cellular response. | | Transporter Binding | The binding of molecules, such as neurotransmitters or drugs, to transporter proteins, which are typically located on cell membranes and involved in reuptake or transport processes. | | Metabolic Spectrum | A profile of the concentrations of various metabolites within a tissue, typically measured using Magnetic Resonance Spectroscopy (MRS). | | PET (Positron Emission Tomography) | An imaging technique that uses radioactive tracers to measure metabolic processes, neurotransmitter binding, or blood flow in the brain, providing insights into neurochemistry. | | MRS (Magnetic Resonance Spectroscopy) | An MRI-based technique used to measure the concentration of specific metabolites within a region of the brain, providing information about tissue chemistry and neuronal integrity. | | N-Acetyl Aspartate (NAA) | A metabolite primarily found in neurons, used as an index of neuronal integrity and density; its reduction can indicate neuronal loss or damage. | | Choline (Cho) | A metabolite related to cell membrane turnover and synthesis; elevated levels can be associated with increased cell proliferation or breakdown. | | Creatine (Cre) | A metabolite involved in energy metabolism within cells; its concentration is often used as a reference point in MRS studies due to its relative stability. | | Glutamate (Glu) | The primary excitatory neurotransmitter in the central nervous system, involved in learning, memory, and synaptic plasticity. | | GABA (Y-aminobutyric acid) | The primary inhibitory neurotransmitter in the central nervous system, responsible for reducing neuronal excitability and promoting calmness. | | Agonist | A substance that binds to a receptor and activates it, mimicking the effect of the natural neurotransmitter. | | Antagonist | A substance that binds to a receptor and blocks its activation by the natural neurotransmitter or agonists. | | Modulator | A substance that alters the activity of a receptor or neurotransmitter system without directly activating or blocking it. | | Voxel | A three-dimensional pixel, representing a unit of volume in a medical image like MRI or fMRI. | | GLM (General Linear Model) | A statistical framework used to analyze neuroimaging data, modeling the relationship between observed brain activity (e.g., BOLD signal) and experimental predictors. | | ROI (Region of Interest) | A specific area or cluster of voxels in the brain selected for analysis, based on anatomical location, functional localization, or prior research. | | ERP (Event-Related Potential) | A measured brain response that is directly related to the occurrence of a specific event or stimulus, typically identified by averaging EEG signals time-locked to the event. | | Time-Frequency Analysis | A signal processing technique used to analyze how the frequency content of a signal changes over time, often applied to EEG data to identify oscillatory patterns. | | Fourier Transform | A mathematical method used to decompose a signal into its constituent frequencies, allowing for the analysis of its spectral content. | | MVPA (Multivariate Pattern Analysis) | An analytical approach that examines patterns of brain activity across multiple voxels simultaneously to decode cognitive states or stimuli, rather than focusing on single-voxel activation. | | RSA (Representational Similarity Analysis) | An analytical method used to compare patterns of brain activity across different experimental conditions or subjects, often by calculating correlations between voxel patterns to understand representational similarity. | | Decoding MVPA | A type of MVPA where a classifier is trained to predict specific mental states or stimuli from patterns of brain activity. | | Functional Connectivity | The statistical correlation or temporal coupling of neural activity between different brain regions, indicating how they interact or work together. | | Resting-State Connectivity | The correlation of spontaneous brain activity between different brain regions when an individual is not engaged in a specific task, often reflecting intrinsic functional networks. | | PPI (Psycho-Physiological Interaction) | An fMRI analysis technique used to investigate how the connectivity between two brain regions changes as a function of psychological context or experimental conditions. | | DCM (Dynamic Causal Modelling) | A model-based approach used to infer directed and causal influences between brain regions, analyzing how experimental manipulations affect the effective connectivity within a network. | | DTI (Diffusion Tensor Imaging) | An MRI technique that measures the diffusion of water molecules in brain tissue to infer the structural connectivity of white matter tracts. | | VBM (Voxel-Based Morphometry) | An MRI analysis technique that quantifies differences in brain tissue volume or density across subjects or groups by comparing Voxel-wise measurements after normalization. | | fNIRS (functional Near-Infrared Spectroscopy) | A non-invasive neuroimaging technique that uses near-infrared light to measure changes in blood oxygenation and blood flow in the brain's superficial layers. | | fTCD (functional Transcranial Doppler) | A non-invasive neuroimaging technique that uses ultrasound to measure blood flow velocity in major cerebral arteries, providing insights into cerebral hemodynamics and lateralization. | | Motor Evoked Potential (MEP) | A muscle response recorded via EMG following transcranial magnetic stimulation (TMS) of the motor cortex, used to assess corticospinal excitability. | | Virtual Lesion | A temporary disruption of neural activity in a specific brain region induced by a technique like TMS, used to infer causal relationships between brain activity and behavior. | | tDCS (transcranial Direct Current Stimulation) | A non-invasive brain stimulation technique that applies a weak, constant electrical current to the scalp to modulate cortical excitability. | | tACS (transcranial Alternating Current Stimulation) | A non-invasive brain stimulation technique that applies an oscillating electrical current to the scalp to modulate brain activity at specific frequencies. | | Neurotransmission | The process by which neurons communicate with each other via chemical signals (neurotransmitters) across synapses. | | Receptor Binding | The process by which a neurotransmitter or drug molecule binds to a specific receptor protein on a cell surface, initiating a cellular response. | | Transporter Binding | The binding of molecules, such as neurotransmitters or drugs, to transporter proteins, which are typically located on cell membranes and involved in reuptake or transport processes. | | Metabolic Spectrum | A profile of the concentrations of various metabolites within a tissue, typically measured using Magnetic Resonance Spectroscopy (MRS). | | N-Acetyl Aspartate (NAA) | A metabolite primarily found in neurons, used as an index of neuronal integrity and density; its reduction can indicate neuronal loss or damage. | | Choline (Cho) | A metabolite related to cell membrane turnover and synthesis; elevated levels can be associated with increased cell proliferation or breakdown. | | Creatine (Cre) | A metabolite involved in energy metabolism within cells; its concentration is often used as a reference point in MRS studies due to its relative stability. | | Glutamate (Glu) | The primary excitatory neurotransmitter in the central nervous system, involved in learning, memory, and synaptic plasticity. | | GABA (Y-aminobutyric acid) | The primary inhibitory neurotransmitter in the central nervous system, responsible for reducing neuronal excitability and promoting calmness. | | Agonist | A substance that binds to a receptor and activates it, mimicking the effect of the natural neurotransmitter. | | Antagonist | A substance that binds to a receptor and blocks its activation by the natural neurotransmitter or agonists. | | Modulator | A substance that alters the activity of a receptor or neurotransmitter system without directly activating or blocking it. | | Voxel | A three-dimensional pixel, representing a unit of volume in a medical image like MRI or fMRI. | | GLM (General Linear Model) | A statistical framework used to analyze neuroimaging data, modeling the relationship between observed brain activity (e.g., BOLD signal) and experimental predictors. | | ROI (Region of Interest) | A specific area or cluster of voxels in the brain selected for analysis, based on anatomical location, functional localization, or prior research. | | ERP (Event-Related Potential) | A measured brain response that is directly related to the occurrence of a specific event or stimulus, typically identified by averaging EEG signals time-locked to the event. | | Time-Frequency Analysis | A signal processing technique used to analyze how the frequency content of a signal changes over time, often applied to EEG data to identify oscillatory patterns. | | Fourier Transform | A mathematical method used to decompose a signal into its constituent frequencies, allowing for the analysis of its spectral content. | | MVPA (Multivariate Pattern Analysis) | An analytical approach that examines patterns of brain activity across multiple voxels simultaneously to decode cognitive states or stimuli, rather than focusing on single-voxel activation. | | RSA (Representational Similarity Analysis) | An analytical method used to compare patterns of brain activity across different experimental conditions or subjects, often by calculating correlations between voxel patterns to understand representational similarity. | | Decoding MVPA | A type of MVPA where a classifier is trained to predict specific mental states or stimuli from patterns of brain activity. | | Functional Connectivity | The statistical correlation or temporal coupling of neural activity between different brain regions, indicating how they interact or work together. | | Resting-State Connectivity | The correlation of spontaneous brain activity between different brain regions when an individual is not engaged in a specific task, often reflecting intrinsic functional networks. | | Psychophysiological Interaction (PPI) | An fMRI analysis technique used to investigate how the connectivity between two brain regions changes as a function of psychological context or experimental conditions. | | DCM (Dynamic Causal Modelling) | A model-based approach used to infer directed and causal influences between brain regions, analyzing how experimental manipulations affect the effective connectivity within a network. | | DTI (Diffusion Tensor Imaging) | An MRI technique that measures the diffusion of water molecules in brain tissue to infer the structural connectivity of white matter tracts. | | VBM (Voxel-Based Morphometry) | An MRI analysis technique that quantifies differences in brain tissue volume or density across subjects or groups by comparing Voxel-wise measurements after normalization. | | fNIRS (functional Near-Infrared Spectroscopy) | A non-invasive neuroimaging technique that uses near-infrared light to measure changes in blood oxygenation and blood flow in the brain's superficial layers. | | fTCD (functional Transcranial Doppler) | A non-invasive neuroimaging technique that uses ultrasound to measure blood flow velocity in major cerebral arteries, providing insights into cerebral hemodynamics and lateralization. | | Neuroenchantment | The tendency to be overly impressed by neuroscience findings, often attributing undue importance or explanatory power to brain-related information. | | Blobology | The practice of oversimplifying fMRI findings by attributing complex cognitive functions to localized 'blobs' of activation without considering network interactions or reverse inference. | | Reverse Inference | The logical fallacy of inferring the presence of a specific cognitive process from the activation of a particular brain region, when that region may be involved in multiple processes. | | Multiple Comparisons Problem | The statistical challenge that arises when performing numerous statistical tests, increasing the probability of obtaining false positive results due to chance. | | Circular Analysis | An analytical approach where the same data are used both to define a region of interest and to test a hypothesis within that region, leading to inflated results and invalid inferences. | | Voodoo Correlations | Unusually high correlations reported between fMRI activity and behavioral or personality measures, often resulting from methodological issues like circular analysis or p-hacking. | | p-hacking | The practice of conducting numerous statistical analyses on a dataset and selectively reporting only those that yield statistically significant results. | | Excess Success | The phenomenon where research findings, particularly in neuroscience, appear too good to be true, often due to selective reporting, publication bias, or flawed analyses. | | Publication Bias | The tendency for studies with positive or significant results to be more likely to be published than studies with null or negative results, potentially skewing the scientific literature. | | Clinical Between-Group Design | An experimental design where participants are divided into groups based on a clinical characteristic (e.g., diagnosis, treatment status) to compare their performance or neural activity. | | Within-Subjects Design | An experimental design where each participant is exposed to all experimental conditions or manipulations, allowing for direct comparison of their responses. | | Lesion Studies | Research that examines the behavioral or cognitive consequences of brain damage to infer the function of the affected brain areas. | | ECoG (Electrocorticography) | A method of recording brain activity directly from the surface of the brain using electrodes placed on the dura mater or cerebral cortex. | | SEEG (Stereoelectroencephalography) | A method of recording brain activity using depth electrodes surgically implanted into specific brain structures. | | DBS (Deep Brain Stimulation) | A neurosurgical procedure involving the implantation of electrodes in specific brain areas to deliver electrical impulses, used to treat various neurological and psychiatric disorders. | | VNS (Vagus Nerve Stimulation) | A treatment that involves implanting a device to stimulate the vagus nerve, used primarily for epilepsy and depression, believed to modulate neurotransmission. | | Neurotransmission | The process by which neurons communicate with each other via chemical signals (neurotransmitters) across synapses. | | Receptor Binding | The process by which a neurotransmitter or drug molecule binds to a specific receptor protein on a cell surface, initiating a cellular response. | | Transporter Binding | The binding of molecules, such as neurotransmitters or drugs, to transporter proteins, which are typically located on cell membranes and involved in reuptake or transport processes. | | Metabolic Spectrum | A profile of the concentrations of various metabolites within a tissue, typically measured using Magnetic Resonance Spectroscopy (MRS). | | N-Acetyl Aspartate (NAA) | A metabolite primarily found in neurons, used as an index of neuronal integrity and density; its reduction can indicate neuronal loss or damage. | | Choline (Cho) | A metabolite related to cell membrane turnover and synthesis; elevated levels can be associated with increased cell proliferation or breakdown. | | Creatine (Cre) | A metabolite involved in energy metabolism within cells; its concentration is often used as a reference point in MRS studies due to its relative stability. | | Glutamate (Glu) | The primary excitatory neurotransmitter in the central nervous system, involved in learning, memory, and synaptic plasticity. | | GABA (Y-aminobutyric acid) | The primary inhibitory neurotransmitter in the central nervous system, responsible for reducing neuronal excitability and promoting calmness. | | Agonist | A substance that binds to a receptor and activates it, mimicking the effect of the natural neurotransmitter. | | Antagonist | A substance that binds to a receptor and blocks its activation by the natural neurotransmitter or agonists. | | Modulator | A substance that alters the activity of a receptor or neurotransmitter system without directly activating or blocking it. |