Comparative imaging study of patients with persistent olfactory dysfunction due to mild COVID-19 using structural and functional MRI.

INTRODUCTION: Persistent post-COVID olfactory dysfunction continues to be studied due to the controversy in its pathophysiology and neuroimaging. MATERIALS AND METHODS: The patients had confirmed mild COVID-19 infection with olfactory dysfunction of more than one month of evolution and they were compared to controls with normal olfaction, assessed using the Sniffin' Sticks Olfactory Test and underwent brain, magnetic resonance imaging (MRI) of the olfactory bulb and olfactory function. RESULTS: A total of 8 patients and 2 controls participated. The average age of the patients was 34.5 years (SD 8.5), and that of the controls was 28.5 (SD 2.1). The average score in the patients' olfactory test was 7.9 points (SD 2.2). In brain and olfactory bulb MRI tests, no morphological differences were found. When evaluated by functional MRI, none of the patients activated the entorhinal area in comparison to the controls, who did show activation at this level. Activation of secondary olfactory areas in cases and controls were as follows: orbitofrontal (25% vs 100%), basal ganglia (25% vs 50%) and insula (38% vs 0%) respectively. CONCLUSIONS: There were no observed morphological changes in the brain MRI. Unlike the controls, none of the patients activated the entorhinal cortex in the olfactory functional MRI.

Cerebellar functional connectivity relates to lower urinary tract function: A 7 Tesla study.

OBJECTIVES: The objective of this study is to explore the functional connectivity (FC) of the cerebellum during the storage phase of micturition, through detecting spontaneous blood-oxygen-level dependent signal between the cerebellum and different brain regions using a high-resolution 7 Tesla magnetic resonance imaging (MRI) scanner. MATERIALS AND METHODS: We recruited healthy individuals with no reported history of neurological disease or lower urinary tract (LUT) symptoms. Participants were asked to drink 500 mL of water and then empty their bladders before entering the MRI scanner. They underwent a T1-weighted anatomical scan, followed by an initial (8 min) empty bladder resting state functional MRI (rs-fMRI) acquisition. Once subjects felt the desire to void, a second rs-fMRI scan was obtained, this time with a full bladder state. We established a priori cerebellar regions of interest from the literature to perform seed-to-voxel analysis using nonparametric statistics based on the Threshold Free Cluster Enhancement method and utilized a voxel threshold of p < 0.05. RESULTS: Twenty individuals (10 male and 10 female) with a median age of 25 years (IQR [3.5]) participated in the study. We placed 31 different 4-mm spherical seeds throughout the cerebellum and assessed their FC with the remainder of the brain. Three of these (left cerebellar tonsil, right posterolateral lobe, right posterior lobe) showed significant differences in connectivity when comparing scans conducted with a full bladder to those with an empty bladder. Additionally, we observed sex differences in FC, with connectivity being higher in women during the empty bladder condition. CONCLUSION: Our initial findings reveal, for the first time, that the connectivity of the cerebellar network is modulated by bladder filling and is associated with LUT function. Unraveling the cerebellum's role in bladder function lays the foundation for a more comprehensive understanding of urinary pathologies affecting this area.

Personalized, parcel-guided non-invasive transcranial magnetic stimulation for the treatment of post-concussive syndrome: safety and proof of concept.

OBJECTIVE: To determine the safety and proof of concept of a parcel-guided, repetitive Transcranial Magnetic Stimulation (rTMS) in patients who develop a heterogeneous array of symptoms, known collectively as post-concussive syndrome (PCS), following traumatic brain injury (TBI). METHODS: We performed a retrospective review of off-label, individualized, parcel-guided rTMS in 19 patients from December 2020 to May 2023. Patients had at least one instance of mild, moderate, or severe TBI and developed symptoms not present prior to injury. rTMS targets were identified based on machine learning connectomic software using functional connectivity anomaly matrices compared to healthy controls. EuroQol (EQ-5D), as a measurement of quality of life, and additional questionnaires dependent on individual's symptoms were submitted prior to, after, and during follow-up from rTMS. RESULTS: Nineteen patients showed improvement in EQ-5D and Rivermead Post Concussion Symptoms Questionnaires - 3 after treatment and follow-up. For nine patients who developed depression, five (55%) attained response and remission based on the Beck Depression Inventory after treatment. Eight of ten patients with anxiety had a clinically significant reduction in Generalized Anxiety Disorder-7 scores during follow-up. CONCLUSION: Parcel-guided rTMS is safe and may be effective in reducing PCS symptoms following TBI and should incite further controlled studies.

The Transdiagnostic Connectome Project: a richly phenotyped open dataset for advancing the study of brain-behavior relationships in psychiatry.

An important aim in psychiatry is the establishment of valid and reliable associations linking profiles of brain functioning to clinically relevant symptoms and behaviors across patient populations. To advance progress in this area, we introduce an open dataset containing behavioral and neuroimaging data from 241 individuals aged 18 to 70, comprising 148 individuals meeting diagnostic criteria for a broad range of psychiatric illnesses and a healthy comparison group of 93 individuals. These data include high-resolution anatomical scans, multiple resting-state, and task-based functional MRI runs. Additionally, participants completed over 50 psychological and cognitive assessments. Here, we detail available behavioral data as well as raw and processed MRI derivatives. Associations between data processing and quality metrics, such as head motion, are reported. Processed data exhibit classic task activation effects and canonical functional network organization. Overall, we provide a comprehensive and analysis-ready transdiagnostic dataset, which we hope will accelerate the identification of illness-relevant features of brain functioning, enabling future discoveries in basic and clinical neuroscience.

A quantitative comparison of atlas parcellations on the human superior temporal sulcus.

The superior temporal sulcus (STS) has a functional topography that has been difficult to characterize through traditional approaches. Automated atlas parcellations may be one solution while also being beneficial for both dimensional reduction and standardizing regions of interest, but they yield very different boundary definitions along the STS. Here we evaluate how well machine learning classifiers can correctly identify six social cognitive tasks from STS activation patterns dimensionally reduced using four popular atlases (Glasser et al., 2016; Gordon et al., 2016; Power et al., 2011 as projected onto the surface by Arslan et al., 2018; Schaefer et al., 2018). Functional data was summarized within each STS parcel in one of four ways, then subjected to leave-one-subject-out cross-validation SVM classification. We found that the classifiers could readily label conditions when data was parcellated using any of the four atlases, evidence that dimensional reduction to parcels did not compromise functional fingerprints. Mean activation for the social conditions was the most effective metric for classification in the right STS, whereas all the metrics classified equally well in the left STS. Interestingly, even atlases constructed from random parcellation schemes (null atlases) classified the conditions with high accuracy. We therefore conclude that the complex activation maps on the STS are readily differentiated at a coarse granular level, despite a strict topography having not yet been identified. Further work is required to identify what features have greatest potential to improve the utility of atlases in replacing functional localizers.

Loneliness and Resting-State Functional Brain Connectivity Among Older Adults: A Proportional Correlation.

OBJECTIVE: Loneliness reportedly increases the risk of dementia, especially Alzheimer's disease (AD). The authors' previous study demonstrated associations between loneliness and structural abnormalities observed in early-stage AD. The present study examined associations between the brain's functional characteristics and loneliness among older adults with concerns about cognitive decline. METHODS: This single-center study included 43 participants (13 with amnestic mild cognitive impairment and 30 with normal cognition). Participants were assessed with the revised University of California Los Angeles (UCLA) Loneliness Scale and underwent resting-state functional MRI. Functional images were preprocessed with the CONN toolbox. The selected seeds were within brain regions reportedly associated with loneliness. One-sample general linear model analysis was performed to examine regressions of UCLA Loneliness Scale scores and functional connectivity between the seeds and regions of interest. RESULTS: The revised UCLA Loneliness Scale scores were positively correlated with functional connectivity between the right hippocampus and left lateral parietal lobe and were negatively correlated with functional connectivity between the left amygdala and left frontal operculum and between the left amygdala and right supramarginal gyrus. Analyses were adjusted for age, sex, and education and scores on the Mini-Mental State Examination and Clinical Dementia Rating scale. CONCLUSIONS: Loneliness was associated with abnormal function of the hippocampus, parts of the parietal lobe and frontal cortex, and the amygdala. These findings may suggest a possible correlation between loneliness and neurological changes associated with dementia.

Cerebral cortex functional reorganization in preschool children with congenital sensorineural hearing loss: a resting-state fMRI study.

Purpose: How cortical functional reorganization occurs after hearing loss in preschool children with congenital sensorineural hearing loss (CSNHL) is poorly understood. Therefore, we used resting-state functional MRI (rs-fMRI) to explore the characteristics of cortical reorganization in these patents. Methods: Sixty-three preschool children with CSNHL and 32 healthy controls (HCs) were recruited, and the Categories of Auditory Performance (CAP) scores were determined at the 6-month follow-up after cochlear implantation (CI). First, rs-fMRI data were preprocessed, and amplitude of low-frequency fluctuation (ALFF) and regional homogeneity (ReHo) were calculated. Second, whole-brain functional connectivity (FC) analysis was performed using bilateral primary auditory cortex as seed points. Finally, Spearman correlation analysis was performed between the differential ALFF, ReHo and FC values and the CAP score. Results: ALFF analysis showed that preschool children with CSNHL had lower ALFF values in the bilateral prefrontal cortex and superior temporal gyrus than HCs, but higher ALFF values in the bilateral thalamus and calcarine gyrus. And correlation analysis showed that some abnormal brain regions were weak negatively correlated with CAP score (p < 0.05). The ReHo values in the bilateral superior temporal gyrus, part of the prefrontal cortex and left insular gyrus were lower, whereas ReHo values in the bilateral thalamus, right caudate nucleus and right precentral gyrus were higher, in children with CSNHL than HCs. However, there was no correlation between ReHo values and the CAP scores (p < 0.05). Using primary auditory cortex (PAC) as seed-based FC further analysis revealed enhanced FC in the visual cortex, proprioceptive cortex and motor cortex. And there were weak negative correlations between the FC values in the bilateral superior temporal gyrus, occipital lobe, left postcentral gyrus and right thalamus were weakly negatively correlated and the CAP score (p < 0.05). Conclusion: After auditory deprivation in preschool children with CSNHL, the local functions of auditory cortex, visual cortex, prefrontal cortex and somatic motor cortex are changed, and the prefrontal cortex plays a regulatory role in this process. There is functional reorganization or compensation between children's hearing and these areas, which may not be conducive to auditory language recovery after CI in deaf children.

Deep learning-driven dyslexia detection model using multi-modality data.

Background: Dyslexia is a neurological disorder that affects an individual's language processing abilities. Early care and intervention can help dyslexic individuals succeed academically and socially. Recent developments in deep learning (DL) approaches motivate researchers to build dyslexia detection models (DDMs). DL approaches facilitate the integration of multi-modality data. However, there are few multi-modality-based DDMs. Methods: In this study, the authors built a DL-based DDM using multi-modality data. A squeeze and excitation (SE) integrated MobileNet V3 model, self-attention mechanisms (SA) based EfficientNet B7 model, and early stopping and SA-based Bi-directional long short-term memory (Bi-LSTM) models were developed to extract features from magnetic resonance imaging (MRI), functional MRI, and electroencephalography (EEG) data. In addition, the authors fine-tuned the LightGBM model using the Hyperband optimization technique to detect dyslexia using the extracted features. Three datasets containing FMRI, MRI, and EEG data were used to evaluate the performance of the proposed DDM. Results: The findings supported the significance of the proposed DDM in detecting dyslexia with limited computational resources. The proposed model outperformed the existing DDMs by producing an optimal accuracy of 98.9%, 98.6%, and 98.8% for the FMRI, MRI, and EEG datasets, respectively. Healthcare centers and educational institutions can benefit from the proposed model to identify dyslexia in the initial stages. The interpretability of the proposed model can be improved by integrating vision transformers-based feature extraction.

Assessment of objective visual function following idebenone administration in patients with leber hereditary optic neuropathy.

PURPOSE: To objectively assess visual function in Leber's Hereditary Optic Neuropathy (LHON) patients; this study evaluated pre- and post-idebenone treatment changes in primary visual cortical (V1) responses using functional magnetic resonance imaging (fMRI), given the challenges in subjective testing due to central retinal ganglion cell damage. STUDY DESIGN: A descriptive study involving four confirmed LHON patients. METHODS: Four patients received 900 mg/day of oral idebenone for 24 weeks. Baseline and post-treatment visual acuity, visual fields, and BOLD fMRI responses while passively viewed drifting contrast pattern visual stimuli were compared with self-reported symptoms. RESULTS: Post-idebenone, one patient showed positive trends across subjective tests, reported symptoms, and fMRI. Two patients had stable symptoms and fMRI responses; one improved on subjective tests, and another worsened slightly. Another patient improved in visual field tests despite worsening symptoms and fMRI trends. CONCLUSION: fMRI may offer a valuable objective measure of visual functions in LHON and appears to be more relevant in assessing symptoms. Further research with more participants is needed to ascertain fMRI's role in developing objective visual assessments and treatment evaluation.

Evaluating functional connectivity differences between DBS ON/OFF states in essential tremor.

Deep brain stimulation (DBS) targeting the ventral intermediate (Vim) nucleus of the thalamus is an effective treatment for essential tremor (ET). We studied 15 â€‹ET patients undergoing DBS to a major input/output tract of the Vim, the dentato-rubro-thalamic tract (DRTt), using resting state functional MRI (rsfMRI) to evaluate connectivity differences between DBS ON and OFF and elucidate significant regions most influential in impacting tremor control and/or concomitant gait ataxia. Anatomical/functional 1.5T MRIs were acquired and replicated for each DBS state. Tremor severity and gait ataxia severity were scored with DBS ON at optimal stimulation parameters and immediately upon DBS OFF. Whole brain analysis was performed using dual regression analysis followed by randomized permutation testing for multiple correction comparison. Regions of interest (ROI) analysis was also performed. All 15 patients had tremor improvement between DBS ON/OFF (p â€‹< â€‹0.001). Whole brain analysis revealed significant connectivity changes between states in the left pre-central gyrus and left supplemental motor area. Group analysis of ROIs revealed that, with threshold p â€‹< â€‹0.05, in DBS ON vs. OFF both tremor duration and tremor improvement were significantly correlated to changes in connectivity. A sub-group analysis of patients with greater ataxia had significantly decreased functional connectivity between multiple ROIs in the cortex and cerebellum when DBS was ON compared to OFF. Stimulation of the DRTt and concordant improvement of tremor resulted in connectivity changes seen in multiple regions outside the motor network; when combined with both structural and electrophysiologic connectivity, this may help to serve as a biomarker to improve DBS targeting and possibly predict outcome.

Analyzing fractal dimension in electroconvulsive therapy: Unraveling complexity in structural and functional neuroimaging.

BACKGROUND: Numerous studies show that electroconvulsive therapy (ECT) induces hippocampal neuroplasticity, but findings are inconsistent regarding its clinical relevance. This study aims to investigate ECT-induced plasticity of anterior and posterior hippocampi using mathematical complexity measures in neuroimaging, namely Higuchi's fractal dimension (HFD) for fMRI time series and the fractal dimension of cortical morphology (FD-CM). Furthermore, we explore the potential of these complexity measures to predict ECT treatment response. METHODS: Twenty patients with a current depressive episode (16 with major depressive disorder and 4 with bipolar disorder) underwent MRI-scans before and after an ECT-series. Twenty healthy controls matched for age and sex were also scanned twice for comparison purposes. Resting-state fMRI data were processed, and HFD was computed for anterior and posterior hippocampi. Group-by-time effects for HFD in anterior and posterior hippocampi were calculated and correlations between HFD changes and improvement in depression severity were examined. For FD-CM analyses, we preprocessed structural MRI with CAT12's surface-based methods. We explored group-by-time effects for FD-CM and the predictive value of baseline HFD and FD-CM for treatment outcome. RESULTS: Patients exhibited a significant increase in bilateral hippocampal HFD from baseline to follow-up scans. Right anterior hippocampal HFD increase was associated with reductions in depression severity. We found no group differences and group-by-time effects in FD-CM. After applying a whole-brain regression analysis, we found that baseline FD-CM in the left temporal pole predicted reduction of overall depression severity after ECT. Baseline hippocampal HFD did not predict treatment outcome. CONCLUSION: This study suggests that HFD and FD-CM are promising imaging markers to investigate ECT-induced neuroplasticity associated with treatment response.

Preventive effect of one-session brief focused attention meditation on state fatigue: Resting state functional magnetic resonance imaging study.

INTRODUCTION: The extended practice of meditation may reduce the influence of state fatigue by changing neurocognitive processing. However, little is known about the preventive effects of one-session brief focused attention meditation (FAM) on state fatigue in healthy participants or its potential neural mechanisms. This study examined the preventive effects of one-session brief FAM on state fatigue and its neural correlates using resting-state functional MRI (rsfMRI) measurements. METHODS: We randomly divided 56 meditation-naïve participants into FAM and control groups. After the first rsfMRI scan, each group performed a 10-minute each condition while wearing a functional near-infrared spectroscopy (fNIRS) device for assessing brain activity. Subsequently, following a second rsfMRI scan, the participants completed a fatigue-inducing task (a Go/NoGo task) for 60 min. We evaluated the temporal changes in the Go/NoGo task performance of participants as an indicator of state fatigue. We then calculated changes in the resting-state functional connectivity (rsFC) of the rsfMRI from before to after each condition and compared them between groups. We also evaluated neural correlates between the changes in rsFC and state fatigue. RESULTS AND DISCUSSION: The fNIRS measurements indicated differences in brain activity during each condition between the FAM and control groups, showing decreased medial prefrontal cortex activity and decreased functional connectivity between the medial prefrontal cortex and middle frontal gyrus. The control group exhibited a decrement in Go/NoGo task performance over time, whereas the FAM group did not. These results, thus, suggested that FAM could prevent state fatigue. Compared with the control group, the rsFC analysis revealed a significant increase in the connectivity between the left dorsomedial prefrontal cortex and right superior parietal lobule in the FAM group, suggesting a modification of attention regulation by cognitive effort. In the control group, increased connectivity was observed between the bilateral posterior cingulate cortex and left inferior occipital gyrus, which might be associated with poor attention regulation and reduced higher-order cognitive function. Additionally, the change in the rsFC of the control group was related to state fatigue. CONCLUSION: Our findings suggested that one session of 10-minute FAM could prevent behavioral state fatigue by employing cognitive effort to modify attention regulation as well as suppressing poor attention regulation and reduced higher-order cognitive function.

Brain representations of affective valence and intensity in sustained pleasure and pain.

Pleasure and pain are two fundamental, intertwined aspects of human emotions. Pleasurable sensations can reduce subjective feelings of pain and vice versa, and we often perceive the termination of pain as pleasant and the absence of pleasure as unpleasant. This implies the existence of brain systems that integrate them into modality-general representations of affective experiences. Here, we examined representations of affective valence and intensity in an functional MRI (fMRI) study (n = 58) of sustained pleasure and pain. We found that the distinct subpopulations of voxels within the ventromedial and lateral prefrontal cortices, the orbitofrontal cortex, the anterior insula, and the amygdala were involved in decoding affective valence versus intensity. Affective valence and intensity predictive models showed significant decoding performance in an independent test dataset (n = 62). These models were differentially connected to distinct large-scale brain networks-the intensity model to the ventral attention network and the valence model to the limbic and default mode networks. Overall, this study identified the brain representations of affective valence and intensity across pleasure and pain, promoting a systems-level understanding of human affective experiences.

Stepwise Functional Brain Architecture Correlates with Atrophy in Progressive Supranuclear Palsy.

BACKGROUND: Stepwise functional connectivity (SFC) detects whole-brain functional couplings of a selected region of interest at increasing link-step topological distances. OBJECTIVE: This study applied SFC to test the hypothesis that stepwise architecture propagating from the disease epicenter would shape patterns of brain atrophy in patients with progressive supranuclear palsy-Richardson's syndrome (PSP-RS). METHODS: Thirty-six patients with PSP-RS and 44 age-matched healthy control subjects underwent brain magnetic resonance imaging on a 3-T scanner. The disease epicenter was defined as the peak of atrophy observed in an independent cohort of 13 cases with postmortem confirmation of PSP pathology and used as seed region for SFC analysis. First, we explored SFC rearrangements in patients with PSP-RS, as compared with age-matched control subjects. Subsequently, we tested SFC architecture propagating from the disease epicenter as a determinant of brain atrophy distribution. RESULTS: The disease epicenter was identified in the left midbrain tegmental region. Compared with age-matched control subjects, patients with PSP-RS showed progressively widespread decreased SFC of the midbrain with striatal and cerebellar regions through direct connections and sensorimotor cortical regions through indirect connections. A correlation was found between average link-step distance from the left midbrain in healthy subjects and brain volumes in patients with PSP-RS (r = 0.38, P < 0.001). CONCLUSIONS: This study provides comprehensive insights into the topology of functional network rearrangements in PSP-RS and demonstrates that the brain architectural topology, as described by SFC propagating from the disease epicenter, shapes the pattern of atrophic changes in PSP-RS. Our findings support the view of a network-based pathology propagation in this primary tauopathy. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Depressed patients with childhood maltreatment display altered intra- and inter-network resting state functional connectivity.

BACKGROUND: Childhood maltreatment (CM) is a major risk factor for the development of major depressive disorder (MDD). To gain more knowledge on how adverse childhood experiences influence the development of brain architecture, we studied functional connectivity (FC) alterations of neural networks of depressed patients with, or without the history of CM. METHODS: Depressed patients with severe childhood maltreatment (n = 18), MDD patients without maltreatment (n = 19), and matched healthy controls (n = 20) were examined with resting state functional MRI. History of maltreatment was assessed with the 28-item Childhood Trauma Questionnaire. Intra- and inter-network FC alterations were evaluated using FMRIB Software Library and CONN toolbox. RESULTS: We found numerous intra- and inter-network FC alterations between the maltreated and the non-maltreated patients. Intra-network FC differences were found in the default mode, visual and auditory networks, and cerebellum. Network modelling revealed several inter-network FC alterations connecting the default mode network with the executive control, salience and cerebellar networks. Increased inter-network FC was found in maltreated patients between the sensory-motor and visual, cerebellar, default mode and salience networks. LIMITATIONS: Relatively small sample size, cross-sectional design, and retrospective self-report questionnaire to assess adverse childhood experiences. CONCLUSIONS: Our findings confirm that severely maltreated depressed patients display numerous alterations of intra- and inter-network FC strengths, not only in their fronto-limbic circuits, but also in sensory-motor, visual, auditory, and cerebellar networks. These functional alterations may explain that maltreated individuals typically display altered perception and are prone to develop functional neurological symptom disorder (conversion disorder) in adulthood.

Disrupted dynamic brain functional connectivity in male cocaine use disorder: Hyperconnectivity, strongly-connected state tendency, and links to impulsivity and borderline traits.

Cocaine use is a major public health problem with serious negative consequences at both the individual and societal levels. Cocaine use disorder (CUD) is associated with cognitive and emotional impairments, often manifesting as alterations in brain functional connectivity (FC). This study employed resting-state functional magnetic resonance imaging (rs-fMRI) to examine dynamic FC in 38 male participants with CUD and 31 matched healthy controls. Using group spatial independent component analysis (group ICA) combined with sliding window approach, we identified two recurring distinct connectivity states: the strongly-connected state (state 1) and weakly-connected state (state 2). CUD patients exhibited significant increased mean dwell and fraction time in state 1, and increased transitions from state 2 to state 1, demonstrated significant strongly-connected state tendency. Our analysis revealed abnormal FC patterns that are state-dependent and state-shared in CUD patients. This study observed hyperconnectivity within the default mode network (DMN) and between DMN and other networks, which varied depending on the state. Furthermore, after adjustment for multiple comparisons, we found significant correlations between these altered dynamic FCs and clinical measures of impulsivity and borderline personality disorder. The disrupted FC and repetitive effects of precuneus and angular gyrus across correlations suggested that they might be the important hub of neural circuits related behaviorally and mentally in CUD. In summary, our study highlighted the potential of these disrupted FC as neuroimaging biomarkers and therapeutic targets, and provided new insights into the understanding of the neurophysiologic mechanisms of CUD.

The relative brain signal variability increases in the behavioral variant of frontotemporal dementia and Alzheimer's disease but not in schizophrenia.

Overlapping symptoms between Alzheimer's disease (AD), behavioral variant of frontotemporal dementia (bvFTD), and schizophrenia (SZ) can lead to misdiagnosis and delays in appropriate treatment, especially in cases of early-onset dementia. To determine the potential of brain signal variability as a diagnostic tool, we assessed the coefficient of variation of the BOLD signal (CVBOLD) in 234 participants spanning bvFTD (n = 53), AD (n = 17), SZ (n = 23), and controls (n = 141). All underwent functional and structural MRI scans. Data unveiled a notable increase in CVBOLD in bvFTD patients across both datasets (local and international, p < 0.05), revealing an association with clinical scores (CDR and MMSE, r = 0.46 and r = -0.48, p < 0.0001). While SZ and control group demonstrated no significant differences, a comparative analysis between AD and bvFTD patients spotlighted elevated CVBOLD in the frontopolar cortices for the latter (p < 0.05). Furthermore, CVBOLD not only presented excellent diagnostic accuracy for bvFTD (AUC 0.78-0.95) but also showcased longitudinal repeatability. During a one-year follow-up, the CVBOLD levels increased by an average of 35% in the bvFTD group, compared to a 2% increase in the control group (p < 0.05). Our findings suggest that CVBOLD holds promise as a biomarker for bvFTD, offering potential for monitoring disease progression and differentiating bvFTD from AD and SZ.

The Concussion, Exercise, and Brain Networks (ConExNet) study: a cohort study aimed at understanding the effects of sub-maximal aerobic exercise on resting state functional brain activity in pediatric concussion.

BACKGROUND: Recent scientific evidence has challenged the traditional "rest-is-best" approach for concussion management. It is now thought that "exercise-is-medicine" for concussion, owing to dozens of studies which demonstrate that sub-maximal, graded aerobic exercise can reduce symptom burden and time to symptom resolution. However, the primary neuropathology of concussion is altered functional brain activity. To date, no studies have examined the effects of sub-maximal aerobic exercise on resting state functional brain activity in pediatric concussion. In addition, although exercise is now more widely prescribed following concussion, its cardiopulmonary response is not yet well understood in this population. Our study has two main goals. The first is to understand whether there are exercise-induced resting state functional brain activity differences in children with concussion vs. healthy controls. The second is to profile the physiological response to exercise and understand whether it differs between groups. METHODS: We will perform a single-center, controlled, prospective cohort study of pediatric concussion at a large, urban children's hospital and academic center. Children with sport-related concussion (aged 12-17 years) will be recruited within 4-weeks of injury by our clinical study team members. Key inclusion criteria include: medical clearance to exercise, no prior concussion or neurological history, and no implants that would preclude MRI. Age- and sex-matched healthy controls will be required to meet the same inclusion criteria and will be recruited through the community. The study will be performed over two visits separated by 24-48 h. Visit 1 involves exercise testing (following the current clinical standard for concussion) and breath-by-breath gas collection using a metabolic cart. Visit 2 involves two functional MRI (fMRI) scans interspersed by 10-minutes of treadmill walking at an intensity calibrated to Visit 1 findings. To address sub-objectives, all participants will be asked to self-report symptoms daily and wear a waist-worn tri-axial accelerometer for 28-days after Visit 2. DISCUSSION: Our study will advance the growing exercise-concussion field by helping us understand whether exercise impacts outcomes beyond symptoms in pediatric concussion. We will also be able to profile the cardiopulmonary response to exercise, which may allow for further understanding (and eventual optimization) of exercise in concussion management. TRIAL REGISTRATION: Not applicable.

Motor network dynamic resting state fMRI connectivity of neurotypical children in regions affected by cerebral palsy.

Background: Normative childhood motor network resting-state fMRI effective connectivity is undefined, yet necessary for translatable dynamic resting-state-network-informed evaluation in pediatric cerebral palsy. Methods: Cross-spectral dynamic causal modeling of resting-state-fMRI was investigated in 50 neurotypically developing 5- to 13-year-old children. Fully connected six-node network models per hemisphere included primary motor cortex, striatum, subthalamic nucleus, globus pallidus internus, thalamus, and contralateral cerebellum. Parametric Empirical Bayes with exhaustive Bayesian model reduction and Bayesian modeling averaging informed the model; Purdue Pegboard Test scores of hand motor behavior were the covariate at the group level to determine the effective-connectivity-functional behavior relationship. Results: Although both hemispheres exhibited similar effective connectivity of motor cortico-basal ganglia-cerebellar networks, magnitudes were slightly greater on the right, except for left-sided connections of the striatum which were more numerous and of opposite polarity. Inter-nodal motor network effective connectivity remained consistent and robust across subjects. Age had a greater impact on connections to the contralateral cerebellum, bilaterally. Motor behavior, however, affected different connections in each hemisphere, exerting a more prominent effect on the left modulatory connections to the subthalamic nucleus, contralateral cerebellum, primary motor cortex, and thalamus. Discussion: This study revealed a consistent pattern of directed resting-state effective connectivity in healthy children aged 5-13 years within the motor network, encompassing cortical, subcortical, and cerebellar regions, correlated with motor skill proficiency. Both hemispheres exhibited similar effective connectivity within motor cortico-basal ganglia-cerebellar networks reflecting inter-nodal signal direction predicted by other modalities, mainly differing from task-dependent studies due to network differences at rest. Notably, age-related changes were more pronounced in connections to the contralateral cerebellum. Conversely, motor behavior distinctly impacted connections in each hemisphere, emphasizing its role in modulating left sided connections to the subthalamic nucleus, contralateral cerebellum, primary motor cortex, and thalamus. Motor network effective connectivity was correlated with motor behavior, validating its physiological significance. This study is the first to evaluate a normative effective connectivity model for the pediatric motor network using resting-state functional MRI correlating with behavior and serves as a foundation for identifying abnormal findings and optimizing targeted interventions like deep brain stimulation, potentially influencing future therapeutic approaches for children with movement disorders.

A machine learning approach for potential Super-Agers identification using neuronal functional connectivity networks.

INTRODUCTION: Aging is often associated with cognitive decline. Understanding neural factors that distinguish adults in midlife with superior cognitive abilities (Positive-Agers) may offer insight into how the aging brain achieves resilience. The goals of this study are to (1) introduce an optimal labeling mechanism to distinguish between Positive-Agers and Cognitive Decliners, and (2) identify Positive-Agers using neuronal functional connectivity networks data and demographics. METHODS: In this study, principal component analysis initially created latent cognitive trajectories groups. A hybrid algorithm of machine learning and optimization was then designed to predict latent groups using neuronal functional connectivity networks derived from resting state functional magnetic resonance imaging. Specifically, the Optimal Labeling with Bayesian Optimization (OLBO) algorithm used an unsupervised approach, iterating a logistic regression function with Bayesian posterior updating. This study encompassed 6369 adults from the UK Biobank cohort. RESULTS: OLBO outperformed baseline models, achieving an area under the curve of 88% when distinguishing between Positive-Agers and cognitive decliners. DISCUSSION: OLBO may be a novel algorithm that distinguishes cognitive trajectories with a high degree of accuracy in cognitively unimpaired adults. Highlights: Design an algorithm to distinguish between a Positive-Ager and a Cognitive-Decliner.Introduce a mathematical definition for cognitive classes based on cognitive tests.Accurate Positive-Ager identification using rsfMRI and demographic data (AUC = 0.88).Posterior default mode network has the highest impact on Positive-Aging odds ratio.