Neuroimaging Techniques, Methodological Rigor, and Brain Structural Principles

Functional Near-Infrared Spectroscopy (fNIRS)

• Definition and Underlying Mechanism:

  • Functional Near-Infrared Spectroscopy (fNIRS) is a non-invasive neuroimaging technique that monitors brain activity by measuring cortical changes in blood oxygen levels (Ferrari & Quaresima, 2012).
  • The technique utilizes near-infrared light to measure variations in the concentration of oxygenated and deoxygenated hemoglobin, which is the iron-containing protein in red blood cells that transports oxygen.
  • Near-infrared light penetrates the skin and skull and is absorbed at differential rates by oxygenated versus deoxygenated hemoglobin molecules.

• Depth Capabilities and Comparisons to fMRI:

  • Functional Near-Infrared Spectroscopy evaluates blood oxygenation dynamics across targeted brain regions during task performance.
  • Advantages over fMRI:
  • Substantially quieter operational sound profile.
  • Does not require participants to remain motionless within a confined, enclosed scanner space.
  • Cortical Depth Limitations:
  • Unlike functional Magnetic Resonance Imaging (fMRI), which captures activity throughout the entire brain, fNIRS is restricted to recording from a depth of only a couple of centimeters ($2\,\text{cm}$) from the cortical surface.

• Empirical Applications and Target Populations:

  • Child Development: Utilized to track cognitive development trajectories in children (Defenderfer et al., 2017; Kerr-German & Buss, 2020).
  • Speech Perception: Employed to measure temporal lobe activation during speech perception in adults (Defenderfer et al., 2017; Kerr-German & Buss, 2020).
  • ADHD Risk Assessment: Applied to analyze functional brain connectivity in toddlers identified as clinically at risk for Attention-Deficit/Hyperactivity Disorder (ADHD) (Kerr-German et al., 2022).

Partial Sleep Deprivation and Neural Activation Dynamics

• Experimental Design and Sample Grouping (Yeung et al., 2018):

  • Research examining partial sleep deprivation in college students split participants based on self-reported sleep duration from the preceding night:
  • Sufficient Sleep Group: Participants obtaining greater than $7\,\text{hours}$ of sleep ($> 7\,\text{hours}$).
  • Insufficient Sleep Group: Participants obtaining less than or equal to $7\,\text{hours}$ of sleep ($\le 7\,\text{hours}$).
  • Both groups executed a demanding cognitive test evaluating working memory.

• Working Memory Performance vs. Neural Activation Patterns:

  • Behavioral Results: Both the sufficient sleep group ($> 7\,\text{hours}$) and insufficient sleep group ($\le 7\,\text{hours}$) demonstrated comparable behavioral test performance on the working memory task.
  • Frontal Lobe Activation Patterns:
  • Only students with sufficient sleep displayed frontal brain activation patterns aligned with normative patterns documented in prior research.
  • Students with insufficient sleep failed to exhibit expected frontal activation patterns.

• Clinical Implications and Performance Failure Thresholds:

  • Measurable working memory performance failures typically require total sleep deprivation or a minimum threshold of $5$ or more consecutive nights of partial sleep deprivation.
  • The frontal activation patterns recorded in partially sleep-deprived students resembled neurobiological patterns observed in psychological disorders such as depression.
  • Findings raise critical questions regarding potential long-term adverse effects of partial sleep deprivation on frontal lobe functionality.

Methodological Rigor, Systematic Bias, and Inclusion in Neuroimaging

• Methodological Evaluation and Contextual Factors:

  • Interpretation of structural or functional neuroimaging outcomes requires rigorous scrutiny of experimental design, analytical techniques, and participant demographics.
  • Cultural background and broad contextual variables alter foundational behavioral, cognitive, and emotional processes (Beins, 2019; Gao et al., 2022; Pugh et al., 2022).
  • Inter-group neurobiological differences are typically subtle and vary by individual, context, and factors involving biological sex (Fine, 2014).

• Unintentional Systematic Biases in Neuroimaging Technologies:

  • Electroencephalography (EEG): Methodological challenges create systematic exclusion of participants based on hair texture, hair density, or specific hairstyles (Choy et al., 2022; Webb et al., 2022).
  • Functional Near-Infrared Spectroscopy (fNIRS): Optical signal detection can be affected by epidermal skin tone, leading to unintended participant exclusion (Webb et al., 2022).

• Historical Exclusions in Preclinical and Clinical Research:

  • Major Depressive Disorder and anxiety spectrum disorders are diagnosed at higher rates in biological females, with biological factors significantly contributing to these disparities (Bangasser & Cuarenta, 2021).
  • Historically, nonhuman animal clinical studies systematically omitted female subjects, creating a significant gap in biomedical research (Bangasser & Cuarenta, 2021; Mogil, 2020; Seydel, 2021).

Biological Sex, Macro-Level Neuroanatomy, and the Brain Mosaic Framework

• Impact of Methodological Lenses on Empirical Interpretation:

  • Both the chosen neuroimaging techniques and the theoretical lenses applied substantially influence how empirical data and research outcomes are interpreted (DeCasien et al., 2022).
  • Regional anatomical differences exist across human brains, but empirical data do not support a distinct binary division separating a "female brain" from a "male brain."

• Conflicting Findings in Large-Scale Structural Datasets:

  • Large-scale structural studies yield conflicting evidence regarding sex-based anatomical variations (Eliot et al., 2021; Lotze et al., 2019; Ritchie et al., 2018; Williams et al., 2021):
  • Inconsistent findings exist regarding whether specific individual brain structures are larger in biological males versus females.
  • Inconsistent findings are reported regarding total brain volume, total gray matter volume, and overall white matter differences.
  • The personal relevance of sex-based structural variations at an individual level is exceptionally small.
  • Numerous interacting biopsychosocial variables contribute to overall brain development and observed neuroanatomical variations (Lotze et al., 2019; Ritchie et al., 2018).

• The Brain Mosaic Framework:

  • Instead of categorizing brains into distinct, sex-typical categories, empirical evidence supports viewing human brain structure as a mosaic (Hyde et al., 2019; Joel et al., 2015).
  • The majority of human brains contain a unique, heterogeneous mix of features and characteristics, some typical for females and others typical for males.

Structural Principles of the Brain

• Bottom-Up Structural Exploration:

  • Central nervous system structures are analyzed sequentially from lower baseline regions up to higher cortical areas (Sections $2.6$ through $2.10$).
  • Psychological exploration focuses on major anatomical regions critical to thought and behavior rather than attempting an exhaustive catalog of every neural structure.

• Functional Multiplicity and General Governing Laws:

  • Many neuroanatomical structures maintain multiple, overlapping functional roles in behavior and cognitive processing.
  • Brain functional analysis centers on general, science-based principles governing neural activity while acknowledging wide individual variation in brain morphology.

Integrative Themes in Psychological Science

• Integrative Theme A: Psychological science relies fundamentally on empirical evidence and continually adapts as new data develop.
• Integrative Theme B: Psychology explains general principles governing behavior while explicitly recognizing individual differences.
• Integrative Theme C: Interacting psychological, biological, social, and cultural factors continuously influence behavior and mental processes.