Functional connectivity development along the sensorimotor-association axis enhances the cortical hierarchy.

Human cortical maturation has been posited to be organized along the sensorimotor-association axis, a hierarchical axis of brain organization that spans from unimodal sensorimotor cortices to transmodal association cortices. Here, we investigate the hypothesis that the development of functional connectivity during childhood through adolescence conforms to the cortical hierarchy defined by the sensorimotor-association axis. We tested this pre-registered hypothesis in four large-scale, independent datasets (total n = 3355; ages 5-23 years): the Philadelphia Neurodevelopmental Cohort (n = 1207), Nathan Kline Institute-Rockland Sample (n = 397), Human Connectome Project: Development (n = 625), and Healthy Brain Network (n = 1126). Across datasets, the development of functional connectivity systematically varied along the sensorimotor-association axis. Connectivity in sensorimotor regions increased, whereas connectivity in association cortices declined, refining and reinforcing the cortical hierarchy. These consistent and generalizable results establish that the sensorimotor-association axis of cortical organization encodes the dominant pattern of functional connectivity development.

The cortical thickness of tricenarian cocaine users assembles features of an octogenarian brain.

It has been suggested that substance use disorders could lead to accelerated biological aging, but only a few neuroimaging studies have investigated this hypothesis so far. In this cross-sectional study, structural neuroimaging was performed to measure cortical thickness (CT) in tricenarian adults with cocaine use disorder (CUD, n1 = 30) and their age-paired controls (YC, n1 = 30), and compare it with octogenarian elder controls (EC, n1 = 20). We found that CT in the right fusiform gyrus was similar between CUD and EC, thinner than the expected values of YC. We also found that regarding CT of the right inferior temporal gyrus, right inferior parietal cortex, and left superior parietal cortex, the CUD group exhibited parameters that fell in between EC and YC groups. Finally, CT of the right pars triangularis bordering with orbitofrontal gyrus, right superior temporal gyrus, and right precentral gyrus were reduced in CUD when contrasted with YC, but those areas were unrelated to CT of EC. Despite the 50-year age gap between our age groups, CT of tricenarian cocaine users assembles features of an octogenarian brain, reinforcing the accelerated aging hypothesis in CUD.

Guillain-Barré syndrome associated with COVID-19: A systematic review.

With the outbreak of coronavirus disease 2019 (COVID-19), the whole world was impacted by a pandemic. With the passage of time and knowledge about the dynamics and viral propagation of this disease, the short-, medium- and long-term repercussions are still being discovered. During this period, it has been learned that various manifestations of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can affect the nervous system. In recent months, a variety of studies and case reports have proposed an association between COVID-19 and Guillain-Barré syndrome (GBS). The present work aims to systematically review the publications available to date to verify the relationship between these two pathologies and the characteristics of post-COVID GBS. There were 156 studies included in this work, resulting in a total of 436 patients. The findings show a mean age of the patients of 61,38 years and a male majority. The GBS symptoms began on average 19 days after the onset of COVID-19 infection. Regarding GBS, the main manifestations found included generalized weakness, reflex reduction, facial paresis/paralysis and hypoesthesia. As expected, the most common result in cerebrospinal fluid (CSF) analysis was albuminocytological dissociation. A pattern of blood analysis findings common to all patients was not observed due to non-standardization of case reports. Regarding electrodiagnostic studies, acute inflammatory demyelinating polyneuropathy (AIDP) appeared as the most common subtype of GBS in this study. There have been reports, to a lesser extent, of acute motor axonal neuropathy (AMAN), acute sensorimotor axonal neuropathy (AMSAN), the pharyngeal-cervical-brachial variant (PCB), and Miller-Fisher syndrome (MFS). The GBS treatment used was mainly intravenous immunoglobulin (IVIG) and plasma exchange (PLEX). Therefore, the present study reports a high prevalence of hospitalization and intensive care units ICU admissions, conjecturing a relationship between the development of GBS and the severity of COVID-19. Despite the severity, most patients showed improvement in GBS symptoms after treatment, and their residual symptoms did not include motor involvement. Therefore, the development of GBS seems to be related to COVID-19 infection, as reported by the present systematic review.

An exploratory study of resting-state functional connectivity of amygdala subregions in posttraumatic stress disorder following trauma in adulthood.

We carried out an exploratory study aimed at identifying differences in resting-state functional connectivity for the amygdala and its subregions, right and left basolateral, centromedial and superficial nuclei, in patients with Posttraumatic Stress Disorder (PTSD), relative to controls. The study included 10 participants with PTSD following trauma in adulthood (9 females), and 10 controls (9 females). The results suggest PTSD was associated with a decreased (negative) functional connectivity between the superficial amygdala and posterior brain regions relative to controls. The differences were observed between right superficial amygdala and right fusiform gyrus, and between left superficial amygdala and left lingual and left middle occipital gyri. The results suggest that among PTSD patients, the worse the PTSD symptoms, the lower the connectivity. The results corroborate the fMRI literature that shows PTSD is associated with weaker amygdala functional connectivity with areas of the brain involved in sensory and perceptual processes. The results also suggest that though the patients traumatic experience occured in adulthood, the presence of early traumatic experiences were associated with negative connectivity between the centromedial amygdala and sensory and perceptual regions. We argue that the understanding of the mechanisms of PTSD symptoms, its behaviors and the effects on quality of life of patients may benefit from the investigation of brain function that underpins sensory and perceptual symptoms associated with the disorder.

Sex differences in brain regional homogeneity during acute abstinence in cocaine use disorder.

There are significant sex differences in the clinical characteristics of cocaine use disorder (CUD). As this is a brain disorder that involves changes in functional connectivity, we investigated the existence of sex differences among people with CUD and controls. We used a data-driven method comparing males (n = 20, CK-M) and females with CUD (n = 20, CK-F) and healthy controls (20 males, HC-M and 20 females, HC-F). The participants undertook a resting-state functional magnetic resonance imaging exam. Regional homogeneity (ReHo) was performed to identify group and sex differences. Persons with CUD of both sexes presented lower ReHo parameters than controls, especially within the parietal lobule. Males with CUD showed higher ReHo than females in three right-side brain areas: postcentral gyrus, putamen and fusiform gyrus. It was found that abstinence symptoms severity was associated with lower ReHo values in the right postcentral gyrus and the right fusiform gyrus. Participants with CUD exhibited altered ReHo parameters compared to controls, similar to what is found in ageing-related disorders. Our data also indicate that cocaine has sex-specific effects on brain functioning when analysing ReHo.

Zika virus congenital microcephaly severity classification and the association of severity with neuropsychomotor development.

BACKGROUND: Zika virus infection during pregnancy is linked to birth defects, most notably microcephaly, which is associated with neurodevelopmental delays. OBJECTIVE: The goals of the study were to propose a method for severity classification of congenital microcephaly based on neuroradiologic findings of MRI scans, and to investigate the association of severity with neuropsychomotor developmental scores. We also propose a semi-automated method for MRI-based severity classification of microcephaly. MATERIALS AND METHODS: We conducted a cross-sectional investigation of 42 infants born with congenital Zika infection. Bayley Scales of Infant and Toddler Development III (Bayley-III) developmental evaluations and MRI scans were carried out at ages 13-39 months (mean: 24.8 months; standard deviation [SD]: 5.8 months). The severity score was generated based on neuroradiologist evaluations of brain malformations. Next, we established a distribution of Zika virus-microcephaly severity score including mild, moderate and severe and investigated the association of severity with neuropsychomotor developmental scores. Finally, we propose a simplified semi-automated procedure for estimating the severity score based only on volumetric measures. RESULTS: The results showed a correlation of r=0.89 (P<0.001) between the Zika virus-microcephaly severity score and the semi-automated method. The trimester of infection did not correlate with the semi-automated method. Neuropsychomotor development correlated with the severity classification based on the radiologic readings and semi-automated method; the more severe the imaging scores, the lower the neuropsychomotor developmental scores. CONCLUSION: These severity classification methods can be used to evaluate severity of microcephaly and possible association with developmental consequences. The semi-automated methods thus provide an alternative for predicting severity of microcephaly based on only one MRI sequence.

Visual Explanation for Identification of the Brain Bases for Developmental Dyslexia on fMRI Data.

Problem: Brain imaging studies of mental health and neurodevelopmental disorders have recently included machine learning approaches to identify patients based solely on their brain activation. The goal is to identify brain-related features that generalize from smaller samples of data to larger ones; in the case of neurodevelopmental disorders, finding these patterns can help understand differences in brain function and development that underpin early signs of risk for developmental dyslexia. The success of machine learning classification algorithms on neurofunctional data has been limited to typically homogeneous data sets of few dozens of participants. More recently, larger brain imaging data sets have allowed for deep learning techniques to classify brain states and clinical groups solely from neurofunctional features. Indeed, deep learning techniques can provide helpful tools for classification in healthcare applications, including classification of structural 3D brain images. The adoption of deep learning approaches allows for incremental improvements in classification performance of larger functional brain imaging data sets, but still lacks diagnostic insights about the underlying brain mechanisms associated with disorders; moreover, a related challenge involves providing more clinically-relevant explanations from the neural features that inform classification. Methods: We target this challenge by leveraging two network visualization techniques in convolutional neural network layers responsible for learning high-level features. Using such techniques, we are able to provide meaningful images for expert-backed insights into the condition being classified. We address this challenge using a dataset that includes children diagnosed with developmental dyslexia, and typical reader children. Results: Our results show accurate classification of developmental dyslexia (94.8%) from the brain imaging alone, while providing automatic visualizations of the features involved that match contemporary neuroscientific knowledge (brain regions involved in the reading process for the dyslexic reader group and brain regions associated with strategic control and attention processes for the typical reader group). Conclusions: Our visual explanations of deep learning models turn the accurate yet opaque conclusions from the models into evidence to the condition being studied.

Increased Glucose Activity in Subgenual Anterior Cingulate and Hippocampus of High Performing Older Adults, Despite Amyloid Burden.

BACKGROUND: Individuals at 80 years of age or above with exceptional memory are considered SuperAgers (SA), an operationalized definition of successful cognitive aging. SA showed increased thickness and altered functional connectivity in the anterior cingulate cortex as a neurobiological signature. However, their metabolic alterations are yet to be uncovered. OBJECTIVE: Herein, a metabolic (FDG-PET), amyloid (PIB-PET), and functional (fMRI) analysis of SA were conducted. METHODS: Ten SA, ten age-matched older adults (C80), and ten cognitively normal middle-aged (C50) adults underwent cognitive testing and multimodal neuroimaging examinations. Anterior and posterior regions of the cingulate cortex and hippocampal areas were primarily examined, then subregions of anterior cingulate were segregated. RESULTS: The SA group showed increased metabolic activity in the left and right subgenual anterior cingulate cortex (sACC, p < 0.005 corrected, bilateral) and bilateral hippocampi (right: p < 0.0005 and left: p < 0.005, both corrected) as compared to that in the C80 group. Amyloid deposition was above threshold in 30% of SA and C80 (p > 0.05). The SA group also presented decreased connectivity between right sACC and posterior cingulate (p < 0.005, corrected) as compared to that of the C80 group. CONCLUSION: These results support the key role of sACC and hippocampus in SA, even in the presence of amyloid deposition. It also suggests that sACC may be used as a potential biomarker in older adults for exceptional memory ability. Further longitudinal studies measuring metabolic biomarkers may help elucidate the interaction between these areas in the cognitive aging process.

Predicting Brain Age at Slice Level: Convolutional Neural Networks and Consequences for Interpretability.

Problem: Chronological aging in later life is associated with brain degeneration processes and increased risk for disease such as stroke and dementia. With a worldwide tendency of aging populations and increased longevity, mental health, and psychiatric research have paid increasing attention to understanding brain-related changes of aging. Recent findings suggest there is a brain age gap (a difference between chronological age and brain age predicted by brain imaging indices); the magnitude of the gap may indicate early onset of brain aging processes and disease. Artificial intelligence has allowed for a narrowing of the gap in chronological and predicted brain age. However, the factors that drive model predictions of brain age are still unknown, and there is not much about these factors that can be gleaned from the black-box nature of machine learning models. The goal of the present study was to test a brain age regression approach that is more amenable to interpretation by researchers and clinicians. Methods: Using convolutional neural networks we trained multiple regressor models to predict brain age based on single slices of magnetic resonance imaging, which included gray matter- or white matter-segmented inputs. We evaluated the trained models in all brain image slices to generate a final prediction of brain age. Unlike whole-brain approaches to classification, the slice-level predictions allows for the identification of which brain slices and associated regions have the largest difference between chronological and neuroimaging-derived brain age. We also evaluated how model predictions were influenced by slice index and plane, participant age and sex, and MRI data collection site. Results: The results show, first, that the specific slice used for prediction affects prediction error (i.e., difference between chronological age and neuroimaging-derived brain age); second, the MRI site-stratified separation of training and test sets removed site effects and also minimized sex effects; third, the choice of MRI slice plane influences the overall error of the model. Conclusion: Compared to whole brain-based predictive models of neuroimaging-derived brain age, slice-based approach improves the interpretability and therefore the reliability of the prediction of brain age using MRI data.

An fMRI study of inhibitory control and the effects of exposure to violence in Latin-American early adolescents: alterations in frontoparietal activation and performance.

We used functional magnetic resonance to investigate the effects of exposure to violence on early adolescent brain function in an inhibitory control task. We investigated the association among scores on self-reported exposure to violence, performance and brain activation. Thirty-seven early adolescents (ages 10-14) from a Latin-American urban region participated in the study. Results showed that recent and chronic exposure to violence was associated with less activation of a network of frontal regions, including the anterior cingulate gyrus and the superior frontal cortex; recent exposure to violence was also associated with less activation of the superior parietal lobe. Results also showed that less activation correlated with more prominent deterioration in the performance in the inhibitory control task (increased latency with time). The findings suggest that early adolescence exposure to violence is associated with differences in activation of a neural network commonly associated with executive function and control. The results underscore the urgency of addressing exposure to violence in adolescence, a period of high susceptibility to the environment, and are discussed in the light of the evidence of the effects of violence on adolescent brain function. Executive function training may be a candidate for targeted cognitive interventions aimed at mitigating these effects.

Decoupling of the Occipitotemporal Cortex and the Brain's Default-Mode Network in Dyslexia and a Role for the Cingulate Cortex in Good Readers: A Brain Imaging Study of Brazilian Children.

The goal of the present study was to investigate intrinsic and reading-related brain function associated with dyslexia and typical readers in monolingual Brazilian children. Two fMRI studies were carried out: a resting-state and a word-reading study. The results show (a) underconnectivity between the occipitotemporal region (visual word form area) and the brain's default-mode network in dyslexic readers and (b) more activation of the anterior cingulate cortex for typical readers relative to dyslexic readers. The findings provide evidence for brain connectivity and function differences in an underrepresented population in fMRI studies of dyslexia; the results suggest atypical intrinsic function, and differences in directed attention processes in dyslexia.

Amygdala-based intrinsic functional connectivity and anxiety disorders in adolescents and young adults.

Anxiety disorders (AD) are the most prevalent group of psychiatric disorders in adolescents and young adults. Nevertheless, the pathophysiology of anxiety disorders is still poorly understood. This study investigated differences in the functional connectivity of intrinsic amygdala-based networks of participants with and without AD. Resting state fMRI data were obtained from 18 participants with an AD and 19 healthy comparison individuals. Psychiatric diagnosis was assessed using standardized structured interviews. The comparison between groups was carried out using functional connectivity maps from six seed regions defined using probabilistic maps bilaterally within the amygdala (basolateral, superficial and centromedial amygdala). We found significant between-group differences in five clusters, which showed aberrant functional connectivity with the left basolateral amygdala: right precentral gyrus, right cingulate gyrus, bilateral precuneus, and right superior frontal gyrus in subjects with AD as compared with the comparison subjects. For the comparison subjects, the correlations between the amygdala and the five clusters were either non-significant, or negative. The present study suggests there is an intrinsic disruption in the communication between left basolateral amygdala and a network of brain regions involved with emotion regulation, and with the default mode network in adolescents and young adults with anxiety disorders.