Search

Closed-loop electrical stimulation has become an important alternative to resective surgery for control of pharmacologically-resistant focal epileptic seizures. Seizure spread across large-scale brain networks, rather than its focal onset, is what commonly leads to major disruptions in sensorimotor and cognitive processing, as well as loss-of-consciousness, one of the main impairing aspects of the disorder. Electrical stimulation, triggered by early detection of seizure onset in epileptogenic zones (EZs), has been applied to prevent spread and its subsequent effects. Here, we show how linear feedback seizure-spread controllability in subject-specific (white-matter tractography) Epileptor network models is affected by variations in brain excitability, network coupling strength, control latency and gain, and actuation targets. Feedback control can qualitatively change the nonlinear seizure dynamics, and the paths to seizure termination and spread prevention. Notably, control onset latency is a critical parameter leading to a phase transition in spread controllability. Consequently, the efficacy of EZ-only actuation is limited depending on network excitability, coupling strength, and practical latencies for detection and actuation. Additional feedback-stabilization control of theoretically-derived optimal node subsets in the network are necessary for spread prevention. Finally, we contrast our linear-feedback controllability assessment with other measures based on minimum-energy (Gramian) controllability and nonlinear pulse-perturbation approaches., Competing Interests: Declarations. Conflict of interest: The authors declare no competing financial interest., (© 2025. The Author(s).)

The cognitive neuroscience of human aging seeks to identify neural mechanisms behind the commonalities and individual differences in age-related behavioral changes. This goal has been pursued predominantly through structural or "task-free" resting-state functional neuroimaging. The former has elucidated the material foundations of behavioral decline, and the latter has provided key insight into how functional brain networks change with age. Crucially, however, neither is able to capture brain activity representing specific cognitive processes as they occur. In contrast, task-based functional imaging allows a direct probe into how aging affects real-time brain-behavior associations in any cognitive domain, from perception to higher-order cognition. Here, we outline why task-based functional neuroimaging must move center stage to better understand the neural bases of cognitive aging. In turn, we sketch a multi-modal, behavior-first research framework that is built upon cognitive experimentation and emphasizes the importance of theory and longitudinal design., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Introduction: The U.S. Study to Protect Brain Health Through Lifestyle Intervention to Reduce Risk (U.S. POINTER) is evaluating lifestyle interventions in older adults at risk for cognitive decline and dementia. Here we characterize the baseline data set of the POINTER Imaging ancillary study., Methods: Participants underwent health and cognitive assessments and neuroimaging with multimodal positron emission tomography (PET) (beta-amyloid [Aβ] and tau) and magnetic resonance imaging (MRI). Framingham risk score (FRS) was used to quantify cardiovascular disease (CVD) risk., Results: A total of 1052 participants (31% from underrepresented ethnoracial groups) were enrolled. Compared to Aβ-, Aβ+ (29%) participants were older, had higher apolipoprotein E (APOE) ε4 carriage rate and white matter hyperintensity volume, and greater temporal tau. FRS was related to MRI measures, but not AD biomarkers. FRS and tau had independent effects on cognition., Discussion: In this heterogenous, at-risk cohort, CVD risk was related to more abnormal brain structure and poorer cognition, representing a putative non-AD (Alzheimer's disease) pathway to brain injury and cognitive decline., Highlights: ·The U.S. Study to Protect Brain Health Through Lifestyle Intervention to Reduce Risk (U.S. POINTER) cohort is enriched for cardiovascular disease (CVD) and poor lifestyle ·POINTER Imaging collected multimodal neuroimaging data in this unique, at-risk cohort ·Amyloid burden was related to age, apolipoprotein E (APOE) ε4 carriage, and measures of disease progression ·Associations between amyloid and tau, and tau and cognition, were relatively weak ·CVD risk and tau pathology were independently related to memory., (© 2024 The Author(s). Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

Converging evidence indicates that the heterogeneity of cognitive profiles may arise through detectable alternations in brain functional connectivity. Despite an unprecedented opportunity to uncover neurobiological subtypes through clustering or subtyping analyses on multi-state functional connectivity, few existing approaches are applicable to accommodate the network topology and unique biological architecture. To address this issue, we propose an innovative Bayesian nonparametric network-variate clustering analysis to uncover subgroups of individuals with homogeneous brain functional network patterns under multiple cognitive states. In light of the existing neuroscience literature, we assume there are unknown state-specific modular structures within functional connectivity. Concurrently, we identify informative network features essential for defining subtypes. To further facilitate practical use, we develop a computationally efficient variational inference algorithm to approximate posterior inference with satisfactory estimation accuracy. Extensive simulations show the superiority of our method. We apply the method to the Adolescent Brain Cognitive Development (ABCD) study, and identify neurodevelopmental subtypes and brain sub-network phenotypes under each state to signal neurobiological heterogeneity, suggesting promising directions for further exploration and investigation in neuroscience., (© The Author(s) 2024. Published by Oxford University Press. All rights reserved. [br]For permissions, please e-mail: journals.permissions@oup.com.)

Test article (TA)-induced seizures represent a major safety concern in drug development. Seizures (altered brain wave [electrophysiological] patterns) present clinically as abnormal consciousness with or without tonic/clonic convulsions (where "tonic" = stiffening and "clonic" = involuntary rhythmical movements). Neuropathological findings following seizures may be detected using many methods. Neuro-imaging may show a structural abnormality underlying seizures, such as focal cortical dysplasia or hippocampal sclerosis in patients with chronic epilepsy. Neural cell type-specific biomarkers in blood or cerebrospinal fluid may highlight neuronal damage and/or glial reactions but are not specific indicators of seizures while serum electrolyte and glucose imbalances may induce seizures. Gross observations and brain weights generally are unaffected by TAs with seizurogenic potential, but microscopic evaluation may reveal seizure-related neuron death in some brain regions (especially neocortex, hippocampus, and/or cerebellum). Current globally accepted best practices for neural sampling in nonclinical general toxicity studies provide a suitable screen for brain regions that are known sites of electrical disruption and/or display seizure-induced neural damage. Conventional nonclinical studies can afford an indication that a TA has a potential seizure liability (via in-life signs and/or microscopic evidence of neuron necrosis), but confirmation requires measuring brain electrical (electroencephalographic) activity in a nonclinical study., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

A recently reported behavioral screen in larval zebrafish for phenocopiers of known anesthetics and associated drugs yielded an isoflavone. Related isoflavones have also been reported as GABA A potentiators. From this, we synthesized a small library of isoflavones and incorporated an in vivo phenotypic approach to perform structure-behavior relationship studies of the screening hit and related analogs via behavioral profiling, patch-clamp experiments, and whole brain imaging. This revealed that analogs effect a range of behavioral responses, including sedation with and without enhancing the acoustic startle response. Interestingly, a subset of compounds effect sedation and enhancement of motor responses to both acoustic and light stimuli. Patch clamp recordings of cells with a human GABA A receptor confirmed that behavior-modulating isoflavones modify the GABA signaling. To better understand these molecules' nuanced effects on behavior, we performed whole brain imaging to reveal that analogs differentially effect neuronal activity. These studies demonstrate a multimodal approach to assessing activities of neuroactives.

Objective: To examine the acute and chronic effects of reducing prolonged sedentary time (ST) with physical activity (PA) on cognitive and brain health., Design: Systematic review and meta-analysis., Data Sources: PubMed, Scopus, CINAHL, PsycINFO, SPORTDiscus, Web of Science, and ProQuest Dissertation and Theses., Eligibility Criteria: Randomised controlled trials (RCTs) published from inception to 17 June 2024, with healthy participants without cognitive impairment or neurological conditions that affect cognitive functioning, aged ≥4 years, testing acute and chronic effects of reducing ST and/or prolonged ST by reallocating ST to PA on cognitive function, brain function, and structure., Results: We included 25 RCTs (n=1289) investigating acute (21 studies) and chronic (4 studies) effects on cognitive function (acute: n=20, chronic: n=4) and brain function (acute: n=7, chronic: n=1); there were no studies on brain structure. Acutely interrupting continuous ST with either multiple or a single PA bout improved cognitive function measured from 3 hours to three consecutive days based on 91 effect sizes ( g =0.17, 95% CI: 0.05 to 0.29, p=0.005, I 2 =45.5%). When comparing single versus multiple PA bouts, only multiple PA bouts yielded a positive effect on cognitive function based on 72 effect sizes ( g =0.20, 95% CI: 0.06 to 0.35, p=0.006; I 2 =48.8%). Chronic studies reported null findings on cognitive function (n=4), with some evidence of improved neural efficiency of the hippocampus (n=1)., Conclusion: Interrupting ST with PA acutely improves cognitive function. The evidence from chronic studies remains inconclusive., Systematic Review Registration: PROSPERO CRD42020200998., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. No commercial re-use. See rights and permissions. Published by BMJ.)

Introduction: We investigated longitudinal associations between self-reported exercise and Alzheimer's disease (AD)-related biomarkers in individuals with autosomal dominant AD (ADAD) mutations., Methods: Participants were 308 ADAD mutation carriers aged 39.7 ± 10.8 years from the Dominantly Inherited Alzheimer's Network. Weekly exercise volume was measured via questionnaire and associations with brain volume (magnetic resonance imaging), cerebrospinal fluid biomarkers, and brain amyloid beta (Aβ) measured by positron emission tomography were investigated., Results: Greater volume of weekly exercise at baseline was associated with slower accumulation of brain Aβ at preclinical disease stages β = -0.16 [-0.23 to -0.08], and a slower decline in multiple brain regions including hippocampal volume β = 0.06 [0.03 to 0.08]., Discussion: Exercise is associated with more favorable profiles of AD-related biomarkers in individuals with ADAD mutations. Exercise may have therapeutic potential for delaying the onset of AD; however, randomized controlled trials are vital to determine a causal relationship before a clinical recommendation of exercise is implemented., Highlights: Greater self-reported weekly exercise predicts slower declines in brain volume in autosomal dominant Alzheimer's disease (ADAD). Greater self-reported weekly exercise predicts slower accumulation of brain amyloid beta in ADAD. Associations varied depending on closeness to estimated symptom onset., (© 2024 The Author(s). Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

Subcortical brain structures are involved in developmental, psychiatric and neurological disorders. Here we performed genome-wide association studies meta-analyses of intracranial and nine subcortical brain volumes (brainstem, caudate nucleus, putamen, hippocampus, globus pallidus, thalamus, nucleus accumbens, amygdala and the ventral diencephalon) in 74,898 participants of European ancestry. We identified 254 independent loci associated with these brain volumes, explaining up to 35% of phenotypic variance. We observed gene expression in specific neural cell types across differentiation time points, including genes involved in intracellular signaling and brain aging-related processes. Polygenic scores for brain volumes showed predictive ability when applied to individuals of diverse ancestries. We observed causal genetic effects of brain volumes with Parkinson's disease and attention-deficit/hyperactivity disorder. Findings implicate specific gene expression patterns in brain development and genetic variants in comorbid neuropsychiatric disorders, which could point to a brain substrate and region of action for risk genes implicated in brain diseases., (© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Background and Objectives: TMEM106B has been proposed as a modifier of disease risk in FTLD-TDP, particularly in GRN pathogenic variant carriers. Furthermore, TMEM106B has been investigated as a disease modifier in the context of healthy aging and across multiple neurodegenerative diseases. The objective of this study was to evaluate and compare the effect of TMEM106B on gray matter volume and cognition in each of the common genetic FTD groups and in patients with sporadic FTD., Methods: Participants were enrolled through the ARTFL/LEFFTDS Longitudinal Frontotemporal Lobar Degeneration (ALLFTD) study, which includes symptomatic and presymptomatic individuals with a pathogenic variant in C9orf72, GRN, MAPT, VCP, TBK1, TARDBP, symptomatic nonpathogenic variant carriers, and noncarrier family controls. All participants were genotyped for the TMEM106B rs1990622 SNP. Cross-sectionally, linear mixed-effects models were fitted to assess an association between TMEM106B and genetic group interaction with each outcome measure (gray matter volume and UDS3-EF for cognition), adjusting for education, age, sex, and CDR+NACC-FTLD sum of boxes. Subsequently, associations between TMEM106B and each outcome measure were investigated within the genetic group. For longitudinal modeling, linear mixed-effects models with time by TMEM106B predictor interactions were fitted., Results: The minor allele of TMEM106B rs1990622, linked to a decreased risk of FTD, associated with greater gray matter volume in GRN pathogenic variant carriers under the recessive dosage model (N = 82, beta = 3.25, 95% CI [0.37-6.19], p = 0.034). This was most pronounced in the thalamus in the left hemisphere (beta = 0.03, 95% CI [0.01-0.06], p = 0.006), with a retained association when considering presymptomatic GRN pathogenic variant carriers only (N = 42, beta = 0.03, 95% CI [0.01-0.05], p = 0.003). The minor allele of TMEM106B rs1990622 also associated with greater cognitive scores among all C9orf72 pathogenic variant carriers (N = 229, beta = 0.36, 95% CI [0.05-0.066], p = 0.021) and in presymptomatic C9orf72 pathogenic variant carriers (N = 106, beta = 0.33, 95% CI [0.03-0.63], p = 0.036), under the recessive dosage model., Discussion: We identified associations of TMEM106B with gray matter volume and cognition in the presence of GRN and C9orf72 pathogenic variants. The association of TMEM106B with outcomes of interest in presymptomatic GRN and C9orf72 pathogenic variant carriers could additionally reflect TMEM106B's effect on divergent pathophysiologic changes before the appearance of clinical symptoms.

Mental illnesses arise from dysfunction in the brain. Although numerous extraneural factors influence these illnesses, ultimately, it is the science of the brain that will lead to novel therapies. Meanwhile, our understanding of this complex organ is incomplete, leading to the oft-repeated trope that neuroscience has yet to make significant contributions to the care of individuals with mental illnesses. This review seeks to counter this narrative, using specific examples of how neuroscientific advances have contributed to progress in mental health care in the past and how current achievements set the stage for further progress in the future., Competing Interests: Declaration of interests K.D. is a member of the Cell editorial board., (Copyright © 2024 Elsevier Inc. All rights reserved.)

When exposed to rhythmic stimulation, the human brain displays rhythmic activity across sensory modalities and regions. Given the ubiquity of this phenomenon, how sensory rhythms are transformed into neural rhythms remains surprisingly inconclusive. An influential model posits that endogenous oscillations entrain to external rhythms, thereby encoding environmental dynamics and shaping perception. However, research on neural entrainment faces multiple challenges, from ambiguous definitions to methodological difficulties when endogenous oscillations need to be identified and disentangled from other stimulus-related mechanisms that can lead to similar phase-locked responses. Yet, recent years have seen novel approaches to overcome these challenges, including computational modeling, insights from dynamical systems theory, sophisticated stimulus designs, and study of neuropsychological impairments. This review outlines key challenges in neural entrainment research, delineates state-of-the-art approaches, and integrates findings from human and animal neurophysiology to provide a broad perspective on the usefulness, validity, and constraints of oscillatory models in brain-environment interaction., Competing Interests: The authors declare no competing financial interests., (Copyright © 2024 the authors.)

Studies exploring the brain correlates of behavioral symptoms in the frontotemporal dementia spectrum (FTD) have mainly searched for linear correlations with single modality neuroimaging data, either structural magnetic resonance imaging (MRI) or fluoro-deoxy-D-glucose positron emission tomography (FDG-PET). We aimed at studying the two imaging modalities in combination to identify nonlinear co-occurring patterns of atrophy and hypometabolism related to behavioral symptoms. We analyzed data from 93 FTD patients who underwent T1-weighted MRI, FDG-PET imaging, and neuropsychological assessment including the Neuropsychiatric Inventory, Frontal Systems Behavior Scale, and Neurobehavioral Rating Scale. We used a data-driven approach to identify the principal components underlying behavioral variability, then related the identified components to brain variability using a newly developed method fusing maps of grey matter volume and FDG metabolism. A component representing apathy, executive dysfunction, and emotional withdrawal was associated with atrophy in bilateral anterior insula and putamen, and with hypometabolism in the right prefrontal cortex. Another component representing the disinhibition versus depression/mutism continuum was associated with atrophy in the right striatum and ventromedial prefrontal cortex for disinhibition, and hypometabolism in the left fronto-opercular region and sensorimotor cortices for depression/mutism. A component representing psychosis was associated with hypometabolism in the prefrontal cortex and hypermetabolism in auditory and visual cortices. Behavioral symptoms in FTD are associated with atrophy and altered metabolism of specific brain regions, especially located in the frontal lobes, in a hierarchical way: apathy and disinhibition are mostly associated with grey matter atrophy, whereas psychotic symptoms are mostly associated with hyper-/hypo-metabolism., Competing Interests: Declarations. Ethics approval and patients consents statement: Written informed consent for the study, approved by the NINDS Institutional Review Board and in accordance with the Declaration of Helsinki, was obtained from a family member. Assent from the patient was always required in all circumstances. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

Flexible action selection requires cognitive control mechanisms capable of mapping the same inputs to different output actions depending on the context. From a neural state-space perspective, this requires a control representation that separates similar input neural states by context. Additionally, for action selection to be robust and time-invariant, information must be stable in time, enabling efficient readout. Here, using EEG decoding methods, we investigate how the geometry and dynamics of control representations constrain flexible action selection in the human brain. Participants performed a context-dependent action selection task. A forced response procedure probed action selection different states in neural trajectories. The result shows that before successful responses, there is a transient expansion of representational dimensionality that separated conjunctive subspaces. Further, the dynamics stabilizes in the same time window, with entry into this stable, high-dimensional state predictive of individual trial performance. These results establish the neural geometry and dynamics the human brain needs for flexible control over behavior., (© 2024. The Author(s).)

To efficiently yet reliably represent and process information, our brains need to produce information-rich signals that differentiate between moments or cognitive states, while also being robust to noise or corruption. For many, though not all, natural systems, these two properties are often inversely related: More information-rich signals are less robust, and vice versa. Here, we examined how these properties change with ongoing cognitive demands. To this end, we applied dimensionality reduction algorithms and pattern classifiers to functional neuroimaging data collected as participants listened to a story, temporally scrambled versions of the story, or underwent a resting state scanning session. We considered two primary aspects of the neural data recorded in these different experimental conditions. First, we treated the maximum achievable decoding accuracy across participants as an indicator of the "informativeness" of the recorded patterns. Second, we treated the number of features (components) required to achieve a threshold decoding accuracy as a proxy for the "compressibility" of the neural patterns (where fewer components indicate greater compression). Overall, we found that the peak decoding accuracy (achievable without restricting the numbers of features) was highest in the intact (unscrambled) story listening condition. However, the number of features required to achieve comparable classification accuracy was also lowest in the intact story listening condition. Taken together, our work suggests that our brain networks flexibly reconfigure according to ongoing task demands and that the activity patterns associated with higher-order cognition and high engagement are both more informative and more compressible than the activity patterns associated with lower-order tasks and lower engagement., Competing Interests: Competing interests statement:The authors declare no competing interest.

Neurophysiology and neuromodulation strive to understand the neural basis of behavior through a one-to-one correspondence between a particular brain and its behavioral output. Within this framework, studies with few subjects but sufficient sample sizes can be both rigorous and impactful., Competing Interests: Declaration of interests W.F.A. holds patents in the domains of neurosurgery and neuromodulation (US9675783B2 and US9675783B2) and is site principal investigator for industry-funded clinical trials of deep brain stimulation and stem cell circuit therapy (Functional Neuromodulation, Inc.; Enspire, Inc.; and Bluerock Therapeutics). S.A.S. is a consultant for Boston Scientific, Zimmer Biomet, Koh Young, Varian Medical, Neuropace, and Sensoria Therapeutic and is cofounder of Motif Neurotech., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Background: With the availability of disease-modifying therapies for Alzheimer's disease (AD), it is important for clinicians to have tests to aid in AD diagnosis, especially when the presence of amyloid pathology is a criterion for receiving treatment., Methods: High-throughput, mass spectrometry-based assays were used to measure %p-tau217 and amyloid beta (Aβ)42/40 ratio in blood samples from 583 individuals with suspected AD (53% positron emission tomography [PET] positive by Centiloid > 25). An algorithm (PrecivityAD2 test) was developed using these plasma biomarkers to identify brain amyloidosis by PET., Results: The area under the receiver operating characteristic curve (AUC-ROC) for %p-tau217 (0.94) was statistically significantly higher than that for p-tau217 concentration (0.91). The AUC-ROC for the PrecivityAD2 test output, the Amyloid Probability Score 2, was 0.94, yielding 88% agreement with amyloid PET. Diagnostic performance of the APS2 was similar by ethnicity, sex, age, and apoE4 status., Discussion: The PrecivityAD2 blood test showed strong clinical validity, with excellent agreement with brain amyloidosis by PET., (© 2024 C2N Diagnostics LLC. Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

Temporal perceptual learning (TPL) constitutes a unique and profound demonstration of neural plasticity within the brain. Our understanding for the neurometabolic changes associated with TPL on the other hand has been limited in part by the use of traditional fMRI approaches. Since plasticity in the visual cortex has been shown to underlie perceptual learning of visual information, we tested the hypothesis that TPL of an auditory interval involves a similar change in plasticity of the auditory pathway and if so, whether these changes take place in a lower-order sensory-specific brain area such as the primary auditory cortex (A1), or a higher-order modality-independent brain area such as the inferior parietal cortex (IPC). This distinction will inform us of the mechanisms underlying perceptual learning as well as the locus of change as it relates to TPL. In the present study, we took advantage of a new technique: proton magnetic resonance spectroscopy (MRS) in combination with psychophysical measures to provide the first evidence of changes in neurometabolic processing following 5 days of temporal discrimination training. We measured the (E)xcitation-to-(I)nhibition ratio as an index of learning in the right IPC and left A1 while participants learned an auditory two-tone discrimination task. During the first day of training, we found a significant task-related increase in functional E/I ratio within the IPC. While the A1 exhibited the opposite pattern of neurochemical activity, this relationship did not reach statistical significance. After timing performance has reached a plateau, there were no further changes to functional E/I. These findings support the hypothesis that improvements in temporal discrimination relies on neuroplastic changes in the IPC, but it is possible that both areas work synergistically to acquire a temporal interval., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Advances in fetal brain neuroimaging, especially fetal neurosonography and brain magnetic resonance imaging (MRI), allow safe and accurate anatomical assessments of fetal brain structures that serve as a foundation for prenatal diagnosis and counseling regarding fetal brain anomalies. Fetal neurosonography strategically assesses fetal brain anomalies suspected by screening ultrasound. Fetal brain MRI has unique technological features that overcome the anatomical limits of smaller fetal brain size and the unpredictable variable of intrauterine motion artifact. Recent studies of fetal brain MRI provide evidence of improved diagnostic and prognostic accuracy, beginning with prenatal diagnosis. Despite technological advances over the last several decades, the combined use of different qualitative structural biomarkers has limitations in providing an accurate prognosis. Quantitative analyses of fetal brain MRIs offer measurable imaging biomarkers that will more accurately associate with clinical outcomes. First-trimester ultrasound opens new opportunities for risk assessment and fetal brain anomaly diagnosis at the earliest time in pregnancy. This review includes a case vignette to illustrate how fetal brain MRI results interpreted by the fetal neurologist can improve diagnostic perspectives. The strength and limitations of conventional ultrasound and fetal brain MRI will be compared with recent research advances in quantitative methods to better correlate fetal neuroimaging biomarkers of neuropathology to predict functional childhood deficits. Discussion of these fetal sonogram and brain MRI advances will highlight the need for further interdisciplinary collaboration using complementary skills to continue improving clinical decision-making following precision medicine principles., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

T1 image is a widely collected imaging sequence in various neuroimaging datasets, but it is rarely used to construct an individual-level brain network. In this study, a novel individualized radiomics-based structural similarity network was proposed from T1 images. In detail, it used voxel-based morphometry to obtain the preprocessed gray matter images, and radiomic features were then extracted on each region of interest in Brainnetome atlas, and an individualized radiomics-based structural similarity network was finally built using the correlational values of radiomic features between any pair of regions of interest. After that, the network characteristics of individualized radiomics-based structural similarity network were assessed, including graph theory attributes, test-retest reliability, and individual identification ability (fingerprinting). At last, two representative applications for individualized radiomics-based structural similarity network, namely mild cognitive impairment subtype discrimination and fluid intelligence prediction, were exemplified and compared with some other networks on large open-source datasets. The results revealed that the individualized radiomics-based structural similarity network displays remarkable network characteristics and exhibits advantageous performances in mild cognitive impairment subtype discrimination and fluid intelligence prediction. In summary, the individualized radiomics-based structural similarity network provides a distinctive, reliable, and informative individualized structural brain network, which can be combined with other networks such as resting-state functional connectivity for various phenotypic and clinical applications., (© The Author(s) 2024. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Cognitive flexibility is an executive functioning skill that develops in childhood, and when impaired, has transdiagnostic implications for psychiatric disorders. To identify how intrinsic neural architecture at rest is linked to cognitive flexibility performance, we used the data-driven method of independent component analysis (ICA) to investigate resting state networks (RSNs) and their whole-brain connectivity associated with levels of cognitive flexibility performance in children. We hypothesized differences by cognitive flexibility performance in RSN connectivity strength in cortico-striatal circuitry, which would manifest via the executive control network, right and left frontoparietal networks (FPN), salience network, default mode network (DMN), and basal ganglia network. We selected participants from the Adolescent Brain Cognitive Development (ABCD) Study who scored at the 25th, ("CF-Low"), 50th ("CF-Average"), or 75th percentiles ("CF-High") on a cognitive flexibility task, were early to middle puberty, and did not exhibit significant psychopathology (n = 967, 47.9% female; ages 9-10). We conducted whole-brain ICA, identifying 14 well-characterized RSNs. Groups differed in connectivity strength in the right FPN, anterior DMN, and posterior DMN. Planned comparisons indicated CF-High had stronger connectivity between right FPN and supplementary motor/anterior cingulate than CF-Low. CF-High had more anti-correlated connectivity between anterior DMN and precuneus than CF-Average. CF-Low had stronger connectivity between posterior DMN and supplementary motor/anterior cingulate than CF-Average. Post-hoc correlations with reaction time by trial type demonstrated significant associations with connectivity. In sum, our results suggest childhood cognitive flexibility performance is associated with DMN and FPN connectivity strength at rest, and that there may be optimal levels of connectivity associated with task performance that vary by network., Competing Interests: Declaration of competing interest All authors declare that they have no conflicts of interest., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Comparative "omics" studies have revealed unique aspects of human neurobiology, yet an evolutionary perspective of the brain N-glycome is lacking. We performed multiregional characterization of rat, macaque, chimpanzee, and human brain N-glycomes using chromatography and mass spectrometry and then integrated these data with complementary glycotranscriptomic data. We found that, in primates, the brain N-glycome has diverged more rapidly than the underlying transcriptomic framework, providing a means for rapidly generating additional interspecies diversity. Our data suggest that brain N-glycome evolution in hominids has been characterized by an overall increase in complexity coupled with a shift toward increased usage of α(2-6)-linked N -acetylneuraminic acid. Moreover, interspecies differences in the cell type expression pattern of key glycogenes were identified, including some human-specific differences, which may underpin this evolutionary divergence. Last, by comparing the prenatal and adult human brain N-glycomes, we uncovered region-specific neurodevelopmental pathways that lead to distinct spatial N-glycosylation profiles in the mature brain.

Background: Disorders of mood and cognition are prevalent, disabling, and notoriously difficult to treat. Fueling this challenge in treatment is a significant gap in our understanding of their neurophysiological basis., Methods: We recorded high-density neural activity from intracranial electrodes implanted in depression-relevant prefrontal cortical regions in 3 human subjects with severe depression. Neural recordings were labeled with depression severity scores across a wide dynamic range using an adaptive assessment that allowed sampling with a temporal frequency greater than that possible with typical rating scales. We modeled these data using regularized regression techniques with region selection to decode depression severity from the prefrontal recordings., Results: Across prefrontal regions, we found that reduced depression severity is associated with decreased low-frequency neural activity and increased high-frequency activity. When constraining our model to decode using a single region, spectral changes in the anterior cingulate cortex best predicted depression severity in all 3 subjects. Relaxing this constraint revealed unique, individual-specific sets of spatiospectral features predictive of symptom severity, reflecting the heterogeneous nature of depression., Conclusions: The ability to decode depression severity from neural activity increases our fundamental understanding of how depression manifests in the human brain and provides a target neural signature for personalized neuromodulation therapies., (Copyright © 2023 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

Reconstructing visual experience from brain responses measured by functional magnetic resonance imaging (fMRI) is a challenging yet important research topic in brain decoding, especially it has proved more difficult to decode visually similar stimuli, such as faces. Although face attributes are known as the key to face recognition, most existing methods generally ignore how to decode facial attributes more precisely in perceived face reconstruction, which often leads to indistinguishable reconstructed faces. To solve this problem, we propose a novel neural decoding framework called VSPnet (voxel2style2pixel) by establishing hierarchical encoding and decoding networks with disentangled latent representations as media, so that to recover visual stimuli more elaborately. And we design a hierarchical visual encoder (named HVE) to pre-extract features containing both high-level semantic knowledge and low-level visual details from stimuli. The proposed VSPnet consists of two networks: Multi-branch cognitive encoder and style-based image generator. The encoder network is constructed by multiple linear regression branches to map brain signals to the latent space provided by the pre-extracted visual features and obtain representations containing hierarchical information consistent to the corresponding stimuli. We make the generator network inspired by StyleGAN to untangle the complexity of fMRI representations and generate images. And the HVE network is composed of a standard feature pyramid over a ResNet backbone. Extensive experimental results on the latest public datasets have demonstrated the reconstruction accuracy of our proposed method outperforms the state-of-the-art approaches and the identifiability of different reconstructed faces has been greatly improved. In particular, we achieve feature editing for several facial attributes in fMRI domain based on the multiview (i.e., visual stimuli and evoked fMRI) latent representations., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

The language environment to which children are exposed has an impact on later language abilities as well as on brain development; however, it is unclear how early such impacts emerge. This study investigates the effects of children's early language environment and socioeconomic status (SES) on brain structure in infancy at 6 and 30 months of age (both sexes included). We used magnetic resonance imaging to quantify concentrations of myelin in specific fiber tracts in the brain. Our central question was whether Language Environment Analysis (LENA) measures from in-home recording devices and SES measures of maternal education predicted myelin concentrations over the course of development. Results indicate that 30-month-old children exposed to larger amounts of in-home adult input showed more myelination in the white matter tracts most associated with language. Right hemisphere regions also show an association with SES, with older children from more highly educated mothers and exposed to more adult input, showing greater myelin concentrations in language-related areas. We discuss these results in relation to the current literature and implications for future research. SIGNIFICANCE STATEMENT This is the first study to look at how brain myelination is impacted by language input and socioeconomic status early in development. We find robust relationships of both factors in language-related brain areas at 30 months of age., (Copyright © 2023 the authors.)

Brain growth in early childhood is reflected in the evolution of proportional cerebrospinal fluid volumes (pCSF), grey matter (pGM), and white matter (pWM). We study brain development as reflected in the relative fractions of these three tissues for a cohort of 388 children that were longitudinally followed between the ages of 18 and 96 months. We introduce statistical methodology (Riemannian Principal Analysis through Conditional Expectation, RPACE) that addresses major challenges that are of general interest for the analysis of longitudinal neuroimaging data, including the sparsity of the longitudinal observations over time and the compositional structure of the relative brain volumes. Applying the RPACE methodology, we find that longitudinal growth as reflected by tissue composition differs significantly for children of mothers with higher and lower maternal education levels., (© 2023 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Flexible behavior requires gating mechanisms that encode only task-relevant information in working memory. Extant literature supports a theoretical division of labor whereby lateral frontoparietal interactions underlie information maintenance and the striatum enacts the gate. Here, we reveal neocortical gating mechanisms in intracranial EEG patients by identifying rapid, within-trial changes in regional and inter-regional activities that predict subsequent behavioral outputs. Results first demonstrate information accumulation mechanisms that extend prior fMRI (i.e., regional high-frequency activity) and EEG evidence (inter-regional theta synchrony) of distributed neocortical networks in working memory. Second, results demonstrate that rapid changes in theta synchrony, reflected in changing patterns of default mode network connectivity, support filtering. Graph theoretical analyses further linked filtering in task-relevant information and filtering out irrelevant information to dorsal and ventral attention networks, respectively. Results establish a rapid neocortical theta network mechanism for flexible information encoding, a role previously attributed to the striatum., (© 2023. The Author(s).)

Purpose: Ultralow-field (ULF) point-of-care MRI systems allow image acquisition without interrupting medical provision, with neonatal clinical care being an important potential application. The ability to measure neonatal brain tissue T 1 is a key enabling technology for subsequent structural image contrast optimization, as well as being a potential biomarker for brain development. Here we describe an optimized strategy for neonatal T 1 mapping at ULF., Methods: Examinations were performed on a 64-mT portable MRI system. A phantom validation experiment was performed, and a total of 33 in vivo exams were acquired from 28 neonates with postmenstrual age ranging from 31 +4 to 49 +0  weeks. Multiple inversion-recovery turbo spin-echo sequences were acquired with differing inversion and repetition times. An analysis pipeline incorporating inter-sequence motion correction generated proton density and T 1 maps. Regions of interest were placed in the cerebral deep gray matter, frontal white matter, and cerebellum. Weighted linear regression was used to predict T 1 as a function of postmenstrual age., Results: Reduction of T 1 with postmenstrual age is observed in all measured brain tissue; the change in T 1 per week and 95% confidence intervals is given by dT 1  = -21 ms/week [-25, -16] (cerebellum), dT 1  = -14 ms/week [-18, -10] (deep gray matter), and dT 1  = -35 ms/week [-45, -25] (white matter)., Conclusion: Neonatal T 1 values at ULF are shorter than those previously described at standard clinical field strengths, but longer than those of adults at ULF. T 1 reduces with postmenstrual age and is therefore a candidate biomarker for perinatal brain development., (© 2022 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.)

Consumer wearables and health monitors, internet-based health and cognitive assessments, and at-home biosample (e.g., saliva and capillary blood) collection kits are increasingly used by public health researchers for large population-based studies without requiring intensive in-person visits. Alongside reduced participant time burden, remote and virtual data collection allows the participation of individuals who live long distances from hospital or university research centers, or who lack access to transportation. Unfortunately, studies that include magnetic resonance neuroimaging are challenging to perform remotely given the infrastructure requirements of MRI scanners, and, as a result, they often omit socially, economically, and educationally disadvantaged individuals. Lower field strength systems (< 100 mT) offer the potential to perform neuroimaging at a participant's home, enabling more accessible and equitable research. Here we report the first use of a low-field MRI "scan van" with an online assessment of paired-associate learning (PAL) to examine associations between brain morphometry and verbal memory performance. In a sample of 67 individuals, 18-93 years of age, imaged at or near their home, we show expected white and gray matter volume trends with age and find significant (p < 0.05 FWE) associations between PAL performance and hippocampus, amygdala, caudate, and thalamic volumes. High-quality data were acquired in 93% of individuals, and at-home scanning was preferred by all individuals with prior MRI at a hospital or research setting. Results demonstrate the feasibility of remote neuroimaging and cognitive data collection, with important implications for engaging traditionally under-represented communities in neuroimaging research., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The predicted utility of information stored in working memory (WM) is hypothesized to influence the strategic allocation of WM resources. Prior work has shown that when information is prioritized, it is remembered with greater precision relative to other remembered items. However, these paradigms often complicate interpretation of the effects of predicted utility on item fidelity due to a concurrent memory load. Likewise, no fMRI studies have examined whether the predicted utility of an item modulates fidelity in the neural representation of items during the memory delay without a concurrent load. In the current study, we used fMRI to investigate whether predicted utility influences fidelity of WM representations in the brain. Using a generative model multivoxel analysis approach to estimate the quality of remembered representations across predicted utility conditions, we observed that items with greater predicted utility are maintained in memory with greater fidelity, even when they are the only item being maintained. Further, we found that this pattern follows a parametric relationship where more predicted utility corresponded to greater fidelity. These precision differences could not be accounted for based on a redistribution of resources among already-remembered items. Rather, we interpret these results in terms of a gating mechanism that allows for pre-allocation of resources based on predicted value alone. This evidence supports a theoretical distinction between resource allocation that occurs as a result of load and resource pre-allocation that occurs as a result of predicted utility., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

The spread of seizures across brain networks is the main impairing factor, often leading to loss-of-consciousness, in people with epilepsy. Despite advances in recording and modeling brain activity, uncovering the nature of seizure spreading dynamics remains an important challenge to understanding and treating pharmacologically resistant epilepsy. To address this challenge, we introduce a new probabilistic model that captures the spreading dynamics in patient-specific complex networks. Network connectivity and interaction time delays between brain areas were estimated from white-matter tractography. The model's computational tractability allows it to play an important complementary role to more detailed models of seizure dynamics. We illustrate model fitting and predictive performance in the context of patient-specific Epileptor networks. We derive the phase diagram of spread size (order parameter) as a function of brain excitability and global connectivity strength, for different patient-specific networks. Phase diagrams allow the prediction of whether a seizure will spread depending on excitability and connectivity strength. In addition, model simulations predict the temporal order of seizure spread across network nodes. Furthermore, we show that the order parameter can exhibit both discontinuous and continuous (critical) phase transitions as neural excitability and connectivity strength are varied. Existence of a critical point, where response functions and fluctuations in spread size show power-law divergence with respect to control parameters, is supported by mean-field approximations and finite-size scaling analyses. Notably, the critical point separates two distinct regimes of spreading dynamics characterized by unimodal and bimodal spread-size distributions. Our study sheds new light on the nature of phase transitions and fluctuations in seizure spreading dynamics. We expect it to play an important role in the development of closed-loop stimulation approaches for preventing seizure spread in pharmacologically resistant epilepsy. Our findings may also be of interest to related models of spreading dynamics in epidemiology, biology, finance, and statistical physics., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Moosavi, Truccolo. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

How the brain derives 3D information from inherently ambiguous visual input remains the fundamental question of human vision. The past two decades of research have addressed this question as a problem of probabilistic inference, the dominant model being maximum-likelihood estimation (MLE). This model assumes that independent depth-cue modules derive noisy but statistically accurate estimates of 3D scene parameters that are combined through a weighted average. Cue weights are adjusted based on the system representation of each module's output variability. Here I demonstrate that the MLE model fails to account for important psychophysical findings and, importantly, misinterprets the just noticeable difference, a hallmark measure of stimulus discriminability, to be an estimate of perceptual uncertainty. I propose a new theory, termed Intrinsic Constraint, which postulates that the visual system does not derive the most probable interpretation of the visual input, but rather, the most stable interpretation amid variations in viewing conditions. This goal is achieved with the Vector Sum model, which represents individual cue estimates as components of a multi-dimensional vector whose norm determines the combined output. This model accounts for the psychophysical findings cited in support of MLE, while predicting existing and new findings that contradict the MLE model. This article is part of a discussion meeting issue 'New approaches to 3D vision'.

17q12 deletion (17q12Del) syndrome is a copy number variant (CNV) disorder associated with neurodevelopmental disorders and renal cysts and diabetes syndrome (RCAD). Using CRISPR/Cas9 genome editing, we generated a mouse model of 17q12Del syndrome on both inbred (C57BL/6N) and outbred (CD-1) genetic backgrounds. On C57BL/6N, the 17q12Del mice had severe head development defects, potentially mediated by haploinsufficiency of Lhx1, a gene within the interval that controls head development. Phenotypes included brain malformations, particularly disruption of the telencephalon and craniofacial defects. On the CD-1 background, the 17q12Del mice survived to adulthood and showed milder craniofacial and brain abnormalities. We report postnatal brain defects using automated magnetic resonance imaging-based morphometry. In addition, we demonstrate renal and blood glucose abnormalities relevant to RCAD. On both genetic backgrounds, we found sex-specific presentations, with male 17q12Del mice exhibiting higher penetrance and more severe phenotypes. Results from these experiments pinpoint specific developmental defects and pathways that guide clinical studies and a mechanistic understanding of the human 17q12Del syndrome. This mouse mutant represents the first and only experimental model to date for the 17q12 CNV disorder. This article has an associated First Person interview with the first author of the paper., Competing Interests: Competing interests The authors declare no competing or financial interests., (© 2022. Published by The Company of Biologists Ltd.)

Intercellular communication between vascular and nerve cells mediated by diffusible proteins has recently emerged as a critical intrinsic program for neural development. However, whether the vascular smooth muscle cell (VSMC) secretome regulates the connectivity of neural circuits remains unknown. Here, we show that conditioned medium from brain VSMC cultures enhances multiple neuronal functions, such as neuritogenesis, neuronal maturation, and survival, thereby improving circuit connectivity. However, protein denaturation by heating compromised these effects. Combined omics analyses of donor VSMC secretomes and recipient neuron transcriptomes revealed that overlapping pathways of extracellular matrix receptor signaling and adhesion molecule integrin binding mediate VSMC-dependent neuronal development. Furthermore, we found that human arterial VSMCs promote neuronal development in multiple ways, including expanding the time window for nascent neurite initiation, increasing neuronal density, and promoting synchronized firing, whereas human umbilical vein VSMCs lack this capability. These in vitro data indicate that brain arteriolar VSMCs may carry direct instructive information for neural development through intercellular communication in vivo., (© 2022. The Author(s).)

Purpose: Low magnetic field systems provide an important opportunity to expand MRI to new and diverse clinical and research study populations. However, a fundamental limitation of low field strength systems is the reduced SNR compared to 1.5 or 3T, necessitating compromises in spatial resolution and imaging time. Most often, images are acquired with anisotropic voxels with low through-plane resolution, which provide acceptable image quality with reasonable scan times, but can impair visualization of subtle pathology., Methods: Here, we describe a super-resolution approach to reconstruct high-resolution isotropic T 2 -weighted images from a series of low-resolution anisotropic images acquired in orthogonal orientations. Furthermore, acquiring each image with an incremented TE allows calculations of quantitative T 2 images without time penalty., Results: Our approach is demonstrated via phantom and in vivo human brain imaging, with simultaneous 1.5 × 1.5 × 1.5 mm 3 T 2 -weighted and quantitative T 2 maps acquired using a clinically feasible approach that combines three acquisition that require approximately 4-min each to collect. Calculated T 2 values agree with reference multiple TE measures with intraclass correlation values of 0.96 and 0.85 in phantom and in vivo measures, respectively, in line with previously reported brain T 2 values at 150 mT, 1.5T, and 3T., Conclusion: Our multi-orientation and multi-TE approach is a time-efficient method for high-resolution T 2 -weighted images for anatomical visualization with simultaneous quantitative T 2 imaging for increased sensitivity to tissue microstructure and chemical composition., (© 2022 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.)

The NIH HEALthy Brain and Cognitive Development (HBCD) study aims to characterize the impact of in utero exposure to substances, and related environmental exposures on child neurodevelopment and health outcomes. A key focus of HBCD is opioid exposure, which has disproportionately affected rural areas. While most opioid use and neonatal abstinence syndrome has been reported outside of large cities, rural communities are often under-represented in large-scale clinical research studies that involve neuroimaging, in-person assessments, or bio-specimen collections. Thus, there exists a likely mismatch between the communities that are the focus of HBCD and those that can participate. Even geographically proximal participants, however, are likely to bias towards higher socioeconomic status given the anticipated study burden and visit frequency. Wearables, 'nearables', and other consumer biosensors, however, are increasingly capable of collecting continuous physiologic and environmental exposure data, facilitating remote assessment. We review the potential of these technologies for remote in situ data collection, and the ability to engage rural, affected communities. While not necessarily a replacement, these technologies offer a compelling complement to traditional 'gold standard' lab-based methods, with significant potential to expand the study's reach and importance., (Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.)

People adjust their learning rate rationally according to local environmental statistics and calibrate such adjustments based on the broader statistical context. To date, no theory has captured the observed range of adaptive learning behaviors or the complexity of its neural correlates. Here, we attempt to do so using a neural network model that learns to map an internal context representation onto a behavioral response via supervised learning. The network shifts its internal context on receiving supervised signals that are mismatched to its output, thereby changing the "state" to which feedback is associated. A key feature of the model is that such state transitions can either increase learning or decrease learning depending on the duration over which the new state is maintained. Sustained state transitions that occur after changepoints facilitate faster learning and mimic network reset phenomena observed in the brain during rapid learning. In contrast, state transitions after one-off outlier events are short lived, thereby limiting the impact of outlying observations on future behavior. State transitions in our model provide the first mechanistic interpretation for bidirectional learning signals, such as the P300, that relate to learning differentially according to the source of surprising events and may also shed light on discrepant observations regarding the relationship between transient pupil dilations and learning. Together, our results demonstrate that dynamic latent state representations can afford normative inference and provide a coherent framework for understanding neural signatures of adaptive learning across different statistical environments. SIGNIFICANCE STATEMENT How humans adjust their sensitivity to new information in a changing world has remained largely an open question. Bridging insights from normative accounts of adaptive learning and theories of latent state representation, here we propose a feedforward neural network model that adjusts its learning rate online by controlling the speed of transitioning its internal state representations. Our model proposes a mechanistic framework for explaining learning under different statistical contexts, explains previously observed behavior and brain signals, and makes testable predictions for future experimental studies., (Copyright © 2022 the authors.)

A hallmark of adaptation in humans and other animals is our ability to control how we think and behave across different settings. Research has characterized the various forms cognitive control can take-including enhancement of goal-relevant information, suppression of goal-irrelevant information, and overall inhibition of potential responses-and has identified computations and neural circuits that underpin this multitude of control types. Studies have also identified a wide range of situations that elicit adjustments in control allocation (e.g., those eliciting signals indicating an error or increased processing conflict), but the rules governing when a given situation will give rise to a given control adjustment remain poorly understood. Significant progress has recently been made on this front by casting the allocation of control as a decision-making problem. This approach has developed unifying and normative models that prescribe when and how a change in incentives and task demands will result in changes in a given form of control. Despite their successes, these models, and the experiments that have been developed to test them, have yet to face their greatest challenge: deciding how to select among the multiplicity of configurations that control can take at any given time. Here, we will lay out the complexities of the inverse problem inherent to cognitive control allocation, and their close parallels to inverse problems within motor control (e.g., choosing between redundant limb movements). We discuss existing solutions to motor control's inverse problems drawn from optimal control theory, which have proposed that effort costs act to regularize actions and transform motor planning into a well-posed problem. These same principles may help shed light on how our brains optimize over complex control configuration, while providing a new normative perspective on the origins of mental effort., (© 2022 Massachusetts Institute of Technology.)

Early childhood is a sensitive period for learning and social skill development. The maturation of cerebral regions underlying social processing lays the foundation for later social-emotional competence. This study explored myelin changes in social brain regions and their association with changes in parent-rated social-emotional development in a cohort of 129 children (64 females, 0-36 months, 77 White). Results reveal a steep increase in myelination throughout the social brain in the first 3 years of life that is significantly associated with social-emotional development scores. These findings add knowledge to the emerging picture of social brain development by describing neural underpinnings of human social behavior. They can contribute to identifying age-/stage-appropriate early life factors in this developmental domain., (© 2021 The Authors. Child Development published by Wiley Periodicals LLC on behalf of Society for Research in Child Development.)

The reference standard for amyloid-PET quantification requires structural MRI (sMRI) for preprocessing in both multi-site research studies and clinical trials. Here we describe rPOP (robust PET-Only Processing), a MATLAB-based MRI-free pipeline implementing non-linear warping and differential smoothing of amyloid-PET scans performed with any of the FDA-approved radiotracers ( 18 F-florbetapir/FBP, 18 F-florbetaben/FBB or 18 F-flutemetamol/FLUTE). Each image undergoes spatial normalization based on weighted PET templates and data-driven differential smoothing, then allowing users to perform their quantification of choice. Prior to normalization, users can choose whether to automatically reset the origin of the image to the center of mass or proceed with the pipeline with the image as it is. We validate rPOP with n = 740 (514 FBP, 182 FBB, 44 FLUTE) amyloid-PET scans from the Imaging Dementia-Evidence for Amyloid Scanning - Brain Health Registry sub-study (IDEAS-BHR) and n = 1,518 scans from the Alzheimer's Disease Neuroimaging Initiative (n = 1,249 FBP, n = 269 FBB), including heterogeneous acquisition and reconstruction protocols. After running rPOP, a standard quantification to extract Standardized Uptake Value ratios and the respective Centiloids conversion was performed. rPOP-based amyloid status (using an independent pathology-based threshold of ≥24.4 Centiloid units) was compared with either local visual reads (IDEAS-BHR, n = 663 with complete valid data and reads available) or with amyloid status derived from an MRI-based PET processing pipeline (ADNI, thresholds of >20/>18 Centiloids for FBP/FBB). Finally, within the ADNI dataset, we tested the linear associations between rPOP- and MRI-based Centiloid values. rPOP achieved accurate warping for N = 2,233/2,258 (98.9%) in the first pass. Of the N = 25 warping failures, 24 were rescued with manual reorientation and origin reset prior to warping. We observed high concordance between rPOP-based amyloid status and both visual reads (IDEAS-BHR, Cohen's k = 0.72 [0.7-0.74], ∼86% concordance) or MRI-pipeline based amyloid status (ADNI, k = 0.88 [0.87-0.89], ∼94% concordance). rPOP- and MRI-pipeline based Centiloids were strongly linearly related (R 2 :0.95, p<0.001), with this association being significantly modulated by estimated PET resolution (β= -0.016, p<0.001). rPOP provides reliable MRI-free amyloid-PET warping and quantification, leveraging widely available software and only requiring an attenuation-corrected amyloid-PET image as input. The rPOP pipeline enables the comparison and merging of heterogeneous datasets and is publicly available at https://github.com/leoiacca/rPOP., (Copyright © 2021. Published by Elsevier Inc.)

Small-molecule modulators of calcineurin signaling have been proposed as potential therapeutics in Down syndrome and Alzheimer's disease. Models predict that in Down syndrome, suppressed calcineurin-NFAT signaling may be mitigated by proINDY, which activates NFAT, the nuclear factor of activated T-cells. Conversely, elevated calcineurin signaling in Alzheimer's disease may be suppressed with the calcineurin inhibitors cyclosporine and tacrolimus. Such small-molecule treatments may have both beneficial and adverse effects. The current study examines the effects of proINDY, cyclosporine and tacrolimus on behavior, using zebrafish larvae as a model system. To suppress calcineurin signaling, larvae were treated with cyclosporine and tacrolimus. We found that these calcineurin inhibitors induced hyperactivity, suppressed visually-guided behaviors, acoustic hyperexcitability and reduced habituation to acoustic stimuli. To activate calcineurin-NFAT signaling, larvae were treated with proINDY. ProINDY treatment reduced activity and stimulated visually-guided behaviors, opposite to the behavioral changes induced by calcineurin inhibitors. The opposing effects suggest that activity and visually-guided behaviors are regulated by the calcineurin-NFAT signaling pathway. A central role of calcineurin-NFAT signaling is further supported by co-treatments of calcineurin inhibitors and proINDY, which had therapeutic effects on activity and visually-guided behaviors. However, these co-treatments adversely increased excitability, suggesting that some behaviors are regulated by other calcineurin signaling pathways. Overall, the developed methodologies provide an efficient high-throughput platform for the evaluation of modulators of calcineurin signaling that restore neural function, while avoiding adverse side effects, in a complex neural system., (Copyright © 2021 The Author(s). Published by Elsevier B.V. All rights reserved.)

Objective: This study aimed to determine if delayed cord clamping (DCC) affected brain myelin water volume fraction (VFm) and neurodevelopment in term infants., Study Design: This was a single-blinded randomized controlled trial of healthy pregnant women with term singleton fetuses randomized at birth to either immediate cord clamping (ICC) (≤ 20 seconds) or DCC (≥ 5 minutes). Follow-up at 12 months of age consisted of blood work for serum iron indices and lead levels, a nonsedated magnetic resonance imaging (MRI), followed within the week by neurodevelopmental testing., Results: At birth, 73 women were randomized into one of two groups: ICC (the usual practice) or DCC (the intervention). At 12 months, among 58 active participants, 41 (80%) had usable MRIs. There were no differences between the two groups on maternal or infant demographic variables. At 12 months, infants who had DCC had increased white matter brain growth in regions localized within the right and left internal capsules, the right parietal, occipital, and prefrontal cortex. Gender exerted no difference on any variables. Developmental testing (Mullen Scales of Early Learning, nonverbal, and verbal composite scores) was not significantly different between the two groups., Conclusion: At 12 months of age, infants who received DCC had greater myelin content in important brain regions involved in motor function, visual/spatial, and sensory processing. A placental transfusion at birth appeared to increase myelin content in the early developing brain., Key Points: · DCC resulted in higher hematocrits in newborn period.. · DCC appears to increase myelin at 12 months.. · Gender did not influence study outcomes.., Competing Interests: None declared., (Thieme. All rights reserved.)

Universal human rights are defined by international agreements, law, foreign policy, and the concept of inherent human dignity. However, rights defined on this basis can be readily subverted by overt and covert disagreements and can be treated as distant geopolitical events rather than bearing on individuals' everyday lives. A robust case for universal human rights is urgently needed and must meet several disparate requirements: (1) a framework that resolves tautological definitions reached solely by mutual, revocable agreement; (2) a rationale that transcends differences in beliefs, creed, and culture; and (3) a personalization that empowers both individuals and governments to further human rights protections. We propose that human rights in existing agreements comprise five elemental types: (1) agency, autonomy, and self-determination; (2) freedom from want; (3) freedom from fear; (4) uniqueness; and (5) unconditionality, including protections for vulnerable populations. We further propose these rights and protections are rooted in fundamental properties of the human brain. We provide a robust, empirical foundation for universal rights based on emerging work in human brain science that we term dignity neuroscience. Dignity neuroscience provides an empirical foundation to support and foster human dignity, universal rights, and their active furtherance by individuals, nations, and international law., (© 2021 The Authors. Annals of the New York Academy of Sciences published by Wiley Periodicals LLC on behalf of New York Academy of Sciences.)

Patients with early-onset Alzheimer's disease (EOAD) are commonly excluded from large-scale observational and therapeutic studies due to their young age, atypical presentation, or absence of pathogenic mutations. The goals of the Longitudinal EOAD Study (LEADS) are to (1) define the clinical, imaging, and fluid biomarker characteristics of EOAD; (2) develop sensitive cognitive and biomarker measures for future clinical and research use; and (3) establish a trial-ready network. LEADS will follow 400 amyloid beta (Aβ)-positive EOAD, 200 Aβ-negative EOnonAD that meet National Institute on Aging-Alzheimer's Association (NIA-AA) criteria for mild cognitive impairment (MCI) or AD dementia, and 100 age-matched controls. Participants will undergo clinical and cognitive assessments, magnetic resonance imaging (MRI), [ 18 F]Florbetaben and [ 18 F]Flortaucipir positron emission tomography (PET), lumbar puncture, and blood draw for DNA, RNA, plasma, serum and peripheral blood mononuclear cells, and post-mortem assessment. To develop more effective AD treatments, scientists need to understand the genetic, biological, and clinical processes involved in EOAD. LEADS will develop a public resource that will enable future planning and implementation of EOAD clinical trials., (© 2021 The Authors. Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

Three-dimensional (3D) neural microtissues are a powerful in vitro paradigm for studying brain development and disease under controlled conditions, while maintaining many key attributes of the in vivo environment. Here, we used primary cortical microtissues to study the effects of neuroinflammation on neural microcircuits. We demonstrated the use of a genetically encoded calcium indicator combined with a novel live-imaging platform to record spontaneous calcium transients in microtissues from day 14-34 in vitro. We implemented graph theory analysis of calcium activity to characterize underlying functional connectivity and community structure of microcircuits, which are capable of capturing subtle changes in network dynamics during early disease states. We found that microtissues cultured for 34 days displayed functional remodeling of microcircuits and that community structure strengthened over time. Lipopolysaccharide, a neuroinflammatory agent, significantly increased functional connectivity and disrupted community structure 5-9 days after exposure. These microcircuit-level changes have broad implications for the role of neuroinflammation in functional dysregulation of neural networks., (© 2021. The Author(s).)

Alzheimer's disease is a neuropsychiatric disorder with devastating clinical and socioeconomic consequences. Since the original description of the neuropathological correlates of the disorder, neuritic plaques and neurofibrillary tangles have been presumed to be critical to the underlying pathophysiology of the illness. The authors review the clinical and neuropathological origins of Alzheimer's disease and trace the evolution of modern biomarkers from their historical roots. They describe how technological innovations such as neuroimaging and biochemical assays have been used to measure and quantify key proteins and lipids in the brain, cerebrospinal fluid, and blood and advance their role as biomarkers of Alzheimer's disease. Together with genomics, these approaches have led to the development of a thematic and focused science in the area of degenerative disorders. The authors conclude by drawing distinctions between legitimate biomarkers of disease and molecular targets for therapeutic intervention and discuss future approaches to this complex neurobehavioral illness.

The goal of this study was to compare brain structure between individuals with generalized anxiety disorder (GAD) and healthy controls. Previous studies have generated inconsistent findings, possibly due to small sample sizes, or clinical/analytic heterogeneity. To address these concerns, we combined data from 28 research sites worldwide through the ENIGMA-Anxiety Working Group, using a single, pre-registered mega-analysis. Structural magnetic resonance imaging data from children and adults (5-90 years) were processed using FreeSurfer. The main analysis included the regional and vertex-wise cortical thickness, cortical surface area, and subcortical volume as dependent variables, and GAD, age, age-squared, sex, and their interactions as independent variables. Nuisance variables included IQ, years of education, medication use, comorbidities, and global brain measures. The main analysis (1020 individuals with GAD and 2999 healthy controls) included random slopes per site and random intercepts per scanner. A secondary analysis (1112 individuals with GAD and 3282 healthy controls) included fixed slopes and random intercepts per scanner with the same variables. The main analysis showed no effect of GAD on brain structure, nor interactions involving GAD, age, or sex. The secondary analysis showed increased volume in the right ventral diencephalon in male individuals with GAD compared to male healthy controls, whereas female individuals with GAD did not differ from female healthy controls. This mega-analysis combining worldwide data showed that differences in brain structure related to GAD are small, possibly reflecting heterogeneity or those structural alterations are not a major component of its pathophysiology., (© 2021. This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply.)

To improve future decisions, people should seek information based on the value of information (VOI), which depends on the current evidence and the reward structure of the upcoming decision. When additional evidence is supplied, people should update the VOI to adjust subsequent information seeking, but the neurocognitive mechanisms of this updating process remain unknown. We used a modified beads task to examine how the VOI is represented and updated in the human brain of both sexes. We theoretically derived, and empirically verified, a normative prediction that the VOI depends on decision evidence and is biased by reward asymmetry. Using fMRI, we found that the subjective VOI is represented in the right dorsolateral prefrontal cortex (DLPFC). Critically, this VOI representation was updated when additional evidence was supplied, showing that the DLPFC dynamically tracks the up-to-date VOI over time. These results provide new insights into how humans adaptively seek information in the service of decision-making. SIGNIFICANCE STATEMENT For adaptive decision-making, people should seek information based on what they currently know and the extent to which additional information could improve the decision outcome, formalized as the VOI. Doing so requires dynamic updating of VOI according to outcome values and newly arriving evidence. We formalize these principles using a normative model and show that information seeking in people adheres to them. Using fMRI, we show that the underlying subjective VOI is represented in the dorsolateral prefrontal cortex and, critically, that it is updated in real time according to newly arriving evidence. Our results reveal the computational and neural dynamics through which evidence and values are combined to inform constantly evolving information-seeking decisions., (Copyright © 2021 the authors.)