Co-Planar Stereotaxic Atlas of the Human Brain—3-Dimensional Proportional System: An Approach to Cerebral Imaging, J. Talairach, P. Tournoux. Georg Thieme Verlag, New York (1988), 122 pp., 130 figs. DM 268

Despite the concern that has been expressed about potential method biases, and the pervasiveness of research settings with the potential to produce them, there is disagreement about whether they really are a problem for researchers in the behavioral sciences. Therefore, the purpose of this review is to explore the current state of knowledge about method biases. First, we explore the meaning of the terms “method” and “method bias” and then we examine whether method biases influence all measures equally. Next, we review the evidence of the effects that method biases have on individual measures and on the covariation between different constructs. Following this, we evaluate the procedural and statistical remedies that have been used to control method biases and provide recommendations for minimizing method bias.

Sources of Method Bias in Social Science Research and Recommendations on How to Control It

Thirty years of brain imaging research has converged to define the brain’s default network—a novel and only recently appreciated brain system that participates in internal modes of cognition Here we synthesize past observations to provide strong evidence that the default network is a specific, anatomically defined brain system preferentially active when individuals are not focused on the external environment Analysis of connectional anatomy in the monkey supports the presence of an interconnected brain system Providing insight into function, the default network is active when individuals are engaged in internally focused tasks including autobiographical memory retrieval, envisioning the future, and conceiving the perspectives of others Probing the functional anatomy of the network in detail reveals that it is best understood as multiple interacting subsystems The medial temporal lobe subsystem provides information from prior experiences in the form of memories and associations that are the building blocks of mental simulation The medial prefrontal subsystem facilitates the flexible use of this information during the construction of self-relevant mental simulations These two subsystems converge on important nodes of integration including the posterior cingulate cortex The implications of these functional and anatomical observations are discussed in relation to possible adaptive roles of the default network for using past experiences to plan for the future, navigate social interactions, and maximize the utility of moments when we are not otherwise engaged by the external world We conclude by discussing the relevance of the default network for understanding mental disorders including autism, schizophrenia, and Alzheimer’s disease

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The Brain's Default Network Anatomy, Function, and Relevance to Disease

I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

The Whale Optimization Algorithm

Background: The apolipoprotein E (APOE, gene; apoE, protein) ε4 allele is the most common identified genetic risk factor for typical late-onset sporadic Alzheimer’s disease (AD). Each APOE ε4 allele roughly triples the relative risk for AD compared to that of the reference allele, APOE ε3. Methods: We have employed hyperspectral fluorescence imaging with an amyloidspecific, conformation-sensing probe, p-FTAA, to elucidate protein aggregate structure and morphology in fresh frozen prefrontal cortex samples from human postmortem AD brain tissue samples from patients homozygous for either APOE ε3 or APOE ε4. Results: As expected APOE ε4/ε4 tissues had significantly larger load of CAA than APOE ε3/ε3. APOE isoform-dependent morphological differences in amyloid plaques were also observed. Amyloid plaques in APOE ε3/ε3 tissue had small spherical cores and large corona while amyloid plaques in APOE ε4/ε4 tissues had large irregular and multilobulated plaques with relatively smaller corona. Despite the different morphologies of their cores, the p-FTAA stained APOE ε3/ε3 amyloid plaque cores had spectral properties identical to those of APOE ε4/ε4 plaque cores. Conclusions: These data support the hypothesis that one mechanism by which the APOE ε4 allele affects AD is by modulating the macrostructure of pathological protein deposits in brain. APOE ε4 is associated with a higher density of amyloid plaques (as compared to APOE ε3). We speculate that multilobulated APOE ε4-associated plaques arise from multiple initiation foci that coalesce as the plaques grow.

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ApoE Alzheimer’s Disease Aβ-amyloid plaque morphology varies according to APOE isotype

Comparative “omics” studies have revealed unique aspects of human neurobiology, yet an evolutionary perspective of the brain N-glycome is lacking. Here, we performed multi-regional characterization of rat, macaque, chimpanzee, and human brain N-glycomes using chromatography and mass spectrometry, then integrated these data with complementary glycotranscriptomic data. We found that in primates the brain N-glycome has evolved more rapidly than the underlying transcriptomic framework, providing a mechanism for generating additional diversity. We show that brain N-glycome evolution in hominids has been characterized by an increase in complexity and α(2-6)-linked N-acetylneuraminic acid along with human-specific cell-type expression of key glycogenes. Finally, by comparing the prenatal and adult human brain N-glycome, we identify region-specific neurodevelopmental pathways that lead to distinct spatial N-glycosylation profiles in the mature brain. One-Sentence Summary Evolution of the human brain N-glycome has been marked by an increase in complexity and a shift in sialic acid linkage.

Human-specific features and developmental dynamics of the brain N-glycome

Pyramidal neurons in the prefrontal cortex integrate inputs that are greater in number and diversity than pyramidal neurons of the primary visual cortex [1]. We have recently characterized the morphology and physiology of layer 3 neurons from area 46 of the dorsolateral prefrontal cortex (dlPFC) and from visual area V1 in the rhesus monkey. Ultra-high resolution confocal imaging and 3D reconstruction revealed that the dendritic arbors of V1 neurons are much smaller and less complex than those of the dlPFC, and possess far fewer dendritic spines. Physiologically, whole cell patch clamp recordings demonstrated that V1 neurons have twice as high mean input resistance and significantly increased action potential firing rates compared to dlPFC neurons. Further, AMPAR-mediated spontaneous excitatory postsynaptic currents (sEPSCs) exhibit significantly faster kinetics and smaller mean amplitudes in V1 compared to dlPFC neurons. We have used compartment modeling with the NEURON simulation environment [2] to investigate the extent to which morphology accounts for the observed physiological differences. We first applied a relatively simple model, including only Hodgkin-Huxley sodium and potassium currents [3], to representative 3D reconstructions of neurons from dlPFC and V1. This model predicts that morphology alone leads to major differences in the attenuation of electrical signals coming into, and leaving, the soma in neurons from the two brain areas. It also predicts that morphology largely accounts for the increased excitability of V1 cells, but not the differences in kinetics and amplitudes of sEPSCs. The model predicts that parameters controlling passive voltage spread, the density of Hodgkin-Huxley channels, and synaptic inputs likely differ between dlPFC and V1 neurons. Yet, the simple model on which these predictions are based reproduces neither the H-current dependent ‘sag’ response during hyperpolarization, nor spike frequency adaptation, both of which are observed in dlPFC and V1 neurons. We have extended the model to include the H current [4], plus a calcium-dependent potassium channel with an associated one-pool source of intracellular calcium [5]. We have tuned the model with a combination of manual and automatic optimization techniques to match the resting potential, input resistance, and overall firing rates of the recorded dlPFC and V1 neurons. As in the simpler model, we find that morphology alone cannot account for observed physiological differences. Therefore, we predict that parameters shaping action potentials and synaptic input differ between dlPFC and V1.

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Patrick R. Hof

Papers

ApoE Alzheimer’s Disease Aβ-amyloid plaque morphology varies according to APOE isotype

Human-specific features and developmental dynamics of the brain N-glycome

Modeling predicts that parameters shaping action potentials and synaptic responses differ in pyramidal neurons of the visual and prefrontal cortices

Short Communication Immunohistochemical Identification of Thrombospondin inNormal HumanBrain andinAlzheimer's Disease

Neurobiologic Basis of Age-Related Dementing Disorders