Most recent paper

Schizophr Bull. 2024 Apr 26:sbae054. doi: 10.1093/schbul/sbae054. Online ahead of print.

ABSTRACT

BACKGROUND: Heterogeneity in the etiology, pathophysiology, and clinical features of schizophrenia challenges clinicians and researchers. A helpful approach could be stratifying patients according to the presence or absence of clinical features of the deficit syndrome (DS). DS is characterized by enduring and primary negative symptoms, a clinically less heterogeneous subtype of the illness, and patients with features of DS are thought to present abnormal brain network characteristics, however, this idea has received limited attention. We investigated functional brain network topology in patients displaying deficit features and those who do not.

DESIGN: We applied graph theory analytics to resting-state functional magnetic resonance imaging data of 61 antipsychotic medication-naïve first episode psychosis patients, 18 DS and 43 non-deficit schizophrenia (NDS), and 72 healthy controls (HC). We quantified small-worldness, global and nodal efficiency measures, shortest path length, nodal local efficiency, and synchronization and contrasted them among the 3 groups.

RESULTS: DS presented decreased network integration and segregation compared to HC and NDS. DS showed lower global efficiency, longer global path lengths, and lower global local efficiency. Nodal efficiency was lower and the shortest path length was longer in DS in default mode, ventral attention, dorsal attention, frontoparietal, limbic, somatomotor, and visual networks compared to HC. Compared to NDS, DS showed lower efficiency and longer shortest path length in default mode, limbic, somatomotor, and visual networks.

CONCLUSIONS: Our data supports increasing evidence, based on topological perturbations of the functional connectome, that deficit syndrome may be a distinct form of the illness.

PMID:38666705 | DOI:10.1093/schbul/sbae054

Cureus. 2024 Mar 25;16(3):e56927. doi: 10.7759/cureus.56927. eCollection 2024 Mar.

ABSTRACT

This review comprehensively explores the evolving role of neuroimaging, specifically magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS), in epilepsy research and clinical practice. Beginning with a concise overview of epilepsy, the discussion emphasizes the crucial importance of neuroimaging in diagnosing and managing this complex neurological disorder. The review delves into the applications of advanced MRI techniques, including high-field MRI, resting-state fMRI, and connectomics, highlighting their impact on refining our understanding of epilepsy's structural and functional dimensions. Additionally, it examines the integration of machine learning in the analysis of intricate neuroimaging data. Moving to the clinical domain, the review outlines the utility of neuroimaging in pre-surgical evaluations and the monitoring of treatment responses and disease progression. Despite significant strides, challenges and limitations are discussed in the routine clinical incorporation of neuroimaging. The review explores promising developments in MRI and MRS technology, potential advancements in imaging biomarkers, and the implications for personalized medicine in epilepsy management. The conclusion underscores the transformative potential of neuroimaging and advocates for continued exploration, collaboration, and technological innovation to propel the field toward a future where tailored, effective interventions improve outcomes for individuals with epilepsy.

PMID:38665706 | PMC:PMC11043648 | DOI:10.7759/cureus.56927

Psychoradiology. 2021 May 11;1(1):42-53. doi: 10.1093/psyrad/kkab003. eCollection 2021 Mar.

ABSTRACT

Resting-state fMRI (rs-fMRI) has emerged as an alternative method to study brain function in human and animal models. In humans, it has been widely used to study psychiatric disorders including schizophrenia, bipolar disorder, autism spectrum disorders, and attention deficit hyperactivity disorders. In this review, rs-fMRI and its advantages over task based fMRI, its currently used analysis methods, and its application in psychiatric disorders using different analysis methods are discussed. Finally, several limitations and challenges of rs-fMRI applications are also discussed.

PMID:38665309 | PMC:PMC10917160 | DOI:10.1093/psyrad/kkab003

Psychoradiology. 2022 Nov 24;2(4):129-145. doi: 10.1093/psyrad/kkac016. eCollection 2022 Dec.

ABSTRACT

There is considerable interest in the significance of structural and functional connections between the two brain hemispheres in terms of both normal function and in relation to psychiatric disorders. In recent years, many studies have used voxel mirrored homotopic connectivity analysis of resting state data to investigate the importance of connectivity between homotopic regions in the brain hemispheres in a range of neuropsychiatric disorders. The current review summarizes findings from these voxel mirrored homotopic connectivity studies in individuals with autism spectrum disorder, addiction, attention deficit hyperactivity disorder, anxiety and depression disorders, and schizophrenia, as well as disorders such as Alzheimer's disease, mild cognitive impairment, epilepsy, and insomnia. Overall, other than attention deficit hyperactivity disorder, studies across psychiatric disorders report decreased homotopic resting state functional connectivity in the default mode, attention, salience, sensorimotor, social cognition, visual recognition, primary visual processing, and reward networks, which are often associated with symptom severity and/or illness onset/duration. Decreased homotopic resting state functional connectivity may therefore represent a transdiagnostic marker for general psychopathology. In terms of disorder specificity, the extensive decreases in homotopic resting state functional connectivity in autism differ markedly from attention deficit hyperactivity disorder, despite both occurring during early childhood and showing extensive co-morbidity. A pattern of more posterior than anterior regions showing reductions in schizophrenia is also distinctive. Going forward, more studies are needed to elucidate the functions of these homotopic functional connections in both health and disorder and focusing on associations with general psychopathology, and not only on disorder specific symptoms.

PMID:38665271 | PMC:PMC11003433 | DOI:10.1093/psyrad/kkac016

iScience. 2024 Apr 4;27(5):109662. doi: 10.1016/j.isci.2024.109662. eCollection 2024 May 17.

ABSTRACT

Atypical perinatal sensory experience in preterm infants is thought to increase their risk of neurodevelopmental disabilities by altering the development of the sensory cortices. Here, we used resting-state fMRI data from preterm and term-born infants scanned between 32 and 48 weeks post-menstrual age to assess the effect of early ex-utero exposure on sensory cortex development. Specifically, we utilized a measure of local correlated-ness called regional homogeneity (ReHo). First, we demonstrated that the brain-wide distribution of ReHo mirrors the known gradient of cortical maturation. Next, we showed that preterm birth differentially reduces ReHo across the primary sensory cortices. Finally, exploratory analyses showed that the reduction of ReHo in the primary auditory cortex of preterm infants is related to increased risk of autism at 18 months. In sum, we show that local connectivity within sensory cortices has different developmental trajectories, is differentially affected by preterm birth, and may be associated with later neurodevelopment.

PMID:38665205 | PMC:PMC11043889 | DOI:10.1016/j.isci.2024.109662

Stat Med. 2024 Apr 25. doi: 10.1002/sim.10091. Online ahead of print.

ABSTRACT

Statistical techniques are needed to analyze data structures with complex dependencies such that clinically useful information can be extracted. Individual-specific networks, which capture dependencies in complex biological systems, are often summarized by graph-theoretical features. These features, which lend themselves to outcome modeling, can be subject to high variability due to arbitrary decisions in network inference and noise. Correlation-based adjacency matrices often need to be sparsified before meaningful graph-theoretical features can be extracted, requiring the data analysts to determine an optimal threshold. To address this issue, we propose to incorporate a flexible weighting function over the full range of possible thresholds to capture the variability of graph-theoretical features over the threshold domain. The potential of this approach, which extends concepts from functional data analysis to a graph-theoretical setting, is explored in a plasmode simulation study using real functional magnetic resonance imaging (fMRI) data from the Autism Brain Imaging Data Exchange (ABIDE) Preprocessed initiative. The simulations show that our modeling approach yields accurate estimates of the functional form of the weight function, improves inference efficiency, and achieves a comparable or reduced root mean square prediction error compared to competitor modeling approaches. This assertion holds true in settings where both complex functional forms underlie the outcome-generating process and a universal threshold value is employed. We demonstrate the practical utility of our approach by using resting-state fMRI data to predict biological age in children. Our study establishes the flexible modeling approach as a statistically principled, serious competitor to ad-hoc methods with superior performance.

PMID:38664934 | DOI:10.1002/sim.10091

AJNR Am J Neuroradiol. 2024 Apr 25. doi: 10.3174/ajnr.A8206. Online ahead of print.

ABSTRACT

BACKGROUND AND PURPOSE: Acute mountain sickness is a series of brain-centered symptoms that occur when rapidly ascending to high altitude. Predicting acute mountain sickness before high-altitude exposure is crucial for protecting susceptible individuals. The present study aimed to evaluate the feasibility of predicting acute mountain sickness after high-altitude exposure by using multimodal brain MR imaging features measured at sea level.

MATERIALS AND METHODS: We recruited 45 healthy sea-level residents who flew to the Qinghai-Tibet Plateau (3650 m). We conducted T1-weighted structural MR imaging, resting-state fMRI, and arterial spin-labeling perfusion MR imaging both at sea level and high altitude. Acute mountain sickness was diagnosed for 5 days using Lake Louise Scoring. Logistic regression with Least Absolute Shrinkage and Selection Operator logistic regression was performed for predicting acute mountain sickness using sea-level MR imaging features. We also validated the predictors by using MR images obtained at high altitude.

RESULTS: The incidence rate of acute mountain sickness was 80.0%. The model achieved an area under the receiver operating characteristic curve of 86.4% (sensitivity = 77.8%, specificity = 100.0%, and P < .001) in predicting acute mountain sickness At sea level, valid predictors included fractional amplitude of low-frequency fluctuations (fALFF) and degree centrality from resting-state fMRI, mainly distributed in the somatomotor network. We further learned that the acute mountain sickness group had lower levels of fALFF in the somatomotor network at high altitude, associated with smaller changes in CSF volume and higher Lake Louise Scoring, specifically relating to fatigue and clinical function.

CONCLUSIONS: Our study found that the somatomotor network function detected by sea-level resting-state fMRI was a crucial predictor for acute mountain sickness and further validated its pathophysiologic impact at high altitude. These findings show promise for pre-exposure prediction, particularly for individuals in need of rapid ascent, and they offer insight into the potential mechanism of acute mountain sickness.

PMID:38663991 | DOI:10.3174/ajnr.A8206

Neuron. 2024 Apr 18:S0896-6273(24)00236-8. doi: 10.1016/j.neuron.2024.03.029. Online ahead of print.

ABSTRACT

Maladaptive feeding behavior is the primary cause of modern obesity. While the causal influence of the lateral hypothalamic area (LHA) on eating behavior has been established in rodents, there is currently no primate-based evidence available on naturalistic eating behaviors. We investigated the role of LHA GABAergic (LHAGABA) neurons in eating using chemogenetics in three macaques. LHAGABA neuron activation significantly increased naturalistic goal-directed behaviors and food motivation, predominantly for palatable food. Positron emission tomography and magnetic resonance spectroscopy validated chemogenetic activation. Resting-state functional magnetic resonance imaging revealed that the functional connectivity (FC) between the LHA and frontal areas was increased, while the FC between the frontal cortices was decreased after LHAGABA neuron activation. Thus, our study elucidates the role of LHAGABA neurons in eating and obesity therapeutics for primates and humans.

PMID:38663401 | DOI:10.1016/j.neuron.2024.03.029

iScience. 2024 Mar 28;27(5):109617. doi: 10.1016/j.isci.2024.109617. eCollection 2024 May 17.

ABSTRACT

Long-term manned spaceflight and extraterrestrial planet settlement become the focus of space powers. However, the potential influence of closed and socially isolating spaceflight on the brain function remains unclear. A 180-day controlled ecological life support system integrated experiment was conducted, establishing a spaceflight analog environment to explore the effect of long-term socially isolating living. Three crewmembers were enrolled and underwent resting-state fMRI scanning before and after the experiment. We performed both seed-based and network-based analyses to investigate the functional connectivity (FC) changes of the default mode network (DMN), considering its key role in multiple higher-order cognitive functions. Compared with normal controls, the leader of crewmembers exhibited significantly reduced within-DMN and between-DMN FC after the experiment, while two others exhibited opposite trends. Moreover, individual differences of FC changes were further supported by evidence from behavioral analyses. The findings may shed new light on the development of psychological protection for space exploration.

PMID:38660401 | PMC:PMC11039341 | DOI:10.1016/j.isci.2024.109617

Int J Clin Health Psychol. 2024 Apr-Jun;24(2):100459. doi: 10.1016/j.ijchp.2024.100459. Epub 2024 Apr 20.

ABSTRACT

BACKGROUND: Individuals with subclinical depression are prone to major depression and experience emotional responses and attentional biases to negative stimuli.

METHOD: In a randomized controlled study (N = 42) using functional magnetic resonance imaging (fMRI), we examined the neurocognitive mechanisms behind mindfulness-based cognitive therapy (MBCT) combining loving-kindness meditation (LKM) on a group with subclinical depression compared with the relaxation group across emotional face n-back (EFNBACK) tasks and resting state. We also collected behavioral and self-reported data to confirm neurocognitive results.

RESULTS: During EFNBACK, the MBCT+LKM group showed greater activation in the left lingual gyrus and right inferior lateral occipital cortex. During rest, the MBCT+LKM group demonstrated increased connectivity of the anterior cingulate cortex and right inferior lateral occipital cortex, right anterior insula and left precentral gyrus. From amplitude of low frequency fluctuation (ALFF) data, activity in brain regions associated with cognitive control decreased and activity in brain regions associated with sensorimotor increased.

CONCLUSION: These results suggest that MBCT+LKM alleviate depression for subclinical individuals through improving executive function when they face negative stimuli.

PMID:38660392 | PMC:PMC11039314 | DOI:10.1016/j.ijchp.2024.100459

BMC Psychiatry. 2024 Apr 24;24(1):313. doi: 10.1186/s12888-024-05777-1.

ABSTRACT

BACKGROUND: Distinguishing untreated major depressive disorder without medication (MDD) from schizophrenia with depressed mood (SZDM) poses a clinical challenge. This study aims to investigate differences in fractional amplitude of low-frequency fluctuations (fALFF) and cognition in untreated MDD and SZDM patients.

METHODS: The study included 42 untreated MDD cases, 30 SZDM patients, and 46 healthy controls (HC). Cognitive assessment utilized the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). Resting-state functional magnetic resonance imaging (rs-fMRI) scans were conducted, and data were processed using fALFF in slow-4 and slow-5 bands.

RESULTS: Significant fALFF changes were observed in four brain regions across MDD, SZDM, and HC groups for both slow-4 and slow-5 fALFF. Compared to SZDM, the MDD group showed increased slow-5 fALFF in the right gyrus rectus (RGR). Relative to HC, SZDM exhibited decreased slow-5 fALFF in the left gyrus rectus (LGR) and increased slow-5 fALFF in the right putamen. Changes in slow-5 fALFF in both RGR and LGR were negatively correlated with RBANS scores. No significant correlations were found between remaining fALFF (slow-4 and slow-5 bands) and RBANS scores in MDD or SZDM groups.

CONCLUSIONS: Alterations in slow-5 fALFF in RGR may serve as potential biomarkers for distinguishing MDD from SZDM, providing preliminary insights into the neural mechanisms of cognitive function in schizophrenia.

PMID:38658896 | DOI:10.1186/s12888-024-05777-1

J Neurosci. 2024 Apr 24:e2024232024. doi: 10.1523/JNEUROSCI.2024-23.2024. Online ahead of print.

ABSTRACT

Alzheimer's disease (AD) is a devastating neurodegenerative disease that affects millions of seniors in the US. Resting-state functional magnetic resonance imaging (rs-fMRI) is widely used to study neurophysiology in AD and its prodromal condition, mild cognitive impairment (MCI). The intrinsic neural timescale (INT), which can be estimated through the magnitude of the autocorrelation of neural signals from rs-fMRI, is thought to quantify the duration that neural information is stored in a local circuit. Such heterogeneity of the timescales forms a basis of the brain functional hierarchy and captures an aspect of circuit dynamics relevant to excitation/inhibition balance, which is broadly relevant for cognitive functions. Given that, we applied rs-fMRI to test whether distinct changes of INT at different hierarchies are present in people with MCI, those progressing to AD (called Converter), and AD patients of both sexes. Linear mixed effect model was implemented to detect altered hierarchical gradients across populations followed by pairwise comparisons to identify regional differences. High similarities between AD and Converter were observed. Specifically, the inferior temporal, caudate, pallidum areas exhibit significant alterations in both AD and Converter. Distinct INT related pathological changes in MCI and AD were found. For AD/Converter, neural information is stored for a longer time in lower hierarchical areas, while higher levels of hierarchy seem to be preferentially impaired in MCI leading to a less pronounced hierarchical gradients. These results inform that the INT holds great potential as an additional measure for AD prediction, even a stable biomarker for clinical diagnosis.Significance Statement We observed high similarities of intrinsic neural timescales (INT) between patients with Alzheimer's Disease (AD) and people that will later progress to AD (called Converter), deviating from cognitively normal individuals. This indicates that pathological excitation/inhibition imbalance already started before the conversion to AD. We also revealed distinct pathophysiological changes in stable mild cognitive impairment (MCI) and AD/Converter. For the AD and Converter, neural information is stored for a longer time in lower brain hierarchical areas; while higher levels of the hierarchy seem to be preferentially impaired in stable MCI. These results suggest the potential for INT as an additional measure for AD prediction, even a stable biomarker for clinical diagnosis.

PMID:38658167 | DOI:10.1523/JNEUROSCI.2024-23.2024

Brain Res. 2024 Apr 22:148956. doi: 10.1016/j.brainres.2024.148956. Online ahead of print.

ABSTRACT

Recent advancements in neuroimaging have illustrated that anterior cruciate ligament (ACL) injuries could impact the central nervous system (CNS), causing neuroplastic changes in the brain beyond the traditionally understood biomechanical consequences. While most of previous functional magnetic resonance imaging (fMRI) studies have focused on localized cortical activity changes post-injury, emerging research has suggested disruptions in functional connectivity across the brain. However, these prior investigations, albeit pioneering, have been constrained by two limitations: a reliance on small-sample participant cohorts, often limited to two to three patients, potentially limiting the generalizability of findings, and an adherence to region of interest based analysis, which may overlook broader network interactions. To address these limitations, our study employed resting-state fMRI to assess whole-brain functional connectivity in 15 ACL-injured patients, comparing them to matched controls using two distinct network analysis methods. Using Network-Based Statistics, we identified widespread reductions in connectivity that spanned across multiple brain regions. Further modular connectivity analysis showed significant decreases in inter-modular connectivity between the sensorimotor and cerebellar modules, and intra-modular connectivity within the default-mode network in ACL-injured patients. Our results thus highlight a shift from localized disruptions to network-wide dysfunctions, suggesting that ACL injuries induce widespread CNS changes. This enhanced understanding has the potential to stimulate the development of strategies aiming to restore functional connectivity and improve recovery outcomes.

PMID:38657888 | DOI:10.1016/j.brainres.2024.148956

Brain Res. 2024 Apr 22:148947. doi: 10.1016/j.brainres.2024.148947. Online ahead of print.

ABSTRACT

Perceived stress is an acknowledged risk factor for subthreshold depression (StD), and fluctuations in perceived stress are thought to disrupt the harmony of brain networks essential for emotional and cognitive functioning. This study aimed to elucidate the relationship between eye-open (EO) and eye-closed (EC) states, perceived stress, and StD. We recruited 27 individuals with StD and 33 healthy controls, collecting resting state fMRI data under both EC and EO conditions. We combined intrinsic connectivity and seed-based functional connectivity analyses to construct the functional network and explore differences between EC and EO conditions. Graph theory analysis revealed weakened connectivity strength in the right superior frontal gyrus (SFG) and right median cingulate and paracingulate gyrus (MCC) among participants with StD, suggesting an important role for these regions in the stress-related emotions dysregulation. Notably, altered SFG connectivity was observed to significantly relate to perceived stress levels in StD, and the SFG connection emerges as a neural mediator potentially influencing the relationship between perceived stress and StD. These findings highlight the role of SFG and MCC in perceived stress and suggest that understanding EC and EO states in relation to these regions is important in the neurobiological framework of StD. This may offer valuable perspectives for early prevention and intervention strategies in mental health disorders.

PMID:38657887 | DOI:10.1016/j.brainres.2024.148947

Neurobiol Aging. 2024 Apr 18;139:82-89. doi: 10.1016/j.neurobiolaging.2024.04.004. Online ahead of print.

ABSTRACT

Alterations in grey matter (GM) and white matter (WM) are associated with memory impairment across the neurocognitive aging spectrum and theorised to spread throughout brain networks. Functional and structural connectivity (FC,SC) may explain widespread atrophy. We tested the effect of SC and FC to the hippocampus on cortical thickness (CT) of connected areas. In 419 (223 F) participants (agemean=73 ± 8) from the Alzheimer's Disease Neuroimaging Initiative, cortical regions associated with memory (Rey Auditory Verbal Learning Test) were identified using Lasso regression. Two structural equation models (SEM), for SC and resting-state FC, were fitted including CT areas, and SC and FC to the left and right hippocampus (LHIP,RHIP). LHIP (β=-0.150,p=<.001) and RHIP (β=-0.139,p=<.001) SC predicted left temporopolar/rhinal CT; RHIP SC predicted right temporopolar/rhinal CT (β=-0.191,p=<.001). LHIP SC predicted right fusiform/parahippocampal (β=-0.104,p=.011) and intraparietal sulcus/superior parietal CT (β=0.101,p=.028). Increased RHIP FC predicted higher left inferior parietal CT (β=0.132,p=.042) while increased LHIP FC predicted lower right fusiform/parahippocampal CT (β=-0.97; p=.023). The hippocampi may be epicentres for cortical thinning through disrupted connectivity.

PMID:38657394 | DOI:10.1016/j.neurobiolaging.2024.04.004

IEEE Trans Med Imaging. 2024 Apr 24;PP. doi: 10.1109/TMI.2024.3392988. Online ahead of print.

ABSTRACT

Functional magnetic resonance imaging (fMRI) is a commonly used technique to measure neural activation. Its application has been particularly important in identifying underlying neurodegenerative conditions such as Parkinson's, Alzheimer's, and Autism. Recent analysis of fMRI data models the brain as a graph and extracts features by graph neural networks (GNNs). However, the unique characteristics of fMRI data require a special design of GNN. Tailoring GNN to generate effective and domain-explainable features remains challenging. In this paper, we propose a contrastive dual-attention block and a differentiable graph pooling method called ContrastPool to better utilize GNN for brain networks, meeting fMRI-specific requirements. We apply our method to 5 resting-state fMRI brain network datasets of 3 diseases and demonstrate its superiority over state-of-the-art baselines. Our case study confirms that the patterns extracted by our method match the domain knowledge in neuroscience literature, and disclose direct and interesting insights. Our contributions underscore the potential of ContrastPool for advancing the understanding of brain networks and neurodegenerative conditions. The source code is available at https://github.com/AngusMonroe/ContrastPool.

PMID:38656865 | DOI:10.1109/TMI.2024.3392988

J Affect Disord. 2024 Apr 21:S0165-0327(24)00652-9. doi: 10.1016/j.jad.2024.04.048. Online ahead of print.

ABSTRACT

INTRODUCTION: Suicide attempters show increased activation in the right superior temporal gyrus (rSTG). Here, we investigated the rSTG functional connectivity (FC) to identify a functional network involved in suicidality and its associations with psychological suicidality risk and resilience factors.

METHODS: The resting state functional magnetic resonance imaging data of 151 healthy individuals from the Human Connectome Project Young Adult database were used to explore the FC of the rSTG with itself and with the rest of the brain. The correlation between the rSTG FC and loneliness and purpose in life scores was assessed with the NIH Toolbox. The effect of sex was also investigated.

RESULTS: The rSTG had a positive FC with bilateral cortical and subcortical regions, including frontal, temporal, parietal, occipital, limbic, and cerebellar regions, and a negative FC with the medulla oblongata. The FC of the rSTG with itself and with the left central operculum were associated with loneliness scores. The within rSTG FC was also negatively correlated with purpose in life scores, although at a trend level. We did not find any effect of sex on FC and its associations with psychological factors.

LIMITATIONS: The cross-sectional design, the limited age range, and the lack of measures of suicidality limit the generalizability of our findings.

CONCLUSIONS: The rSTG functional network is associated with loneliness and purpose in life. Together with the existing literature on suicide, this supports the idea that neural activity of rSTG may contribute to suicidality by modulating risk and resilience factors associated with suicidality.

PMID:38653349 | DOI:10.1016/j.jad.2024.04.048

J Asthma Allergy. 2024 Apr 18;17:383-389. doi: 10.2147/JAA.S454807. eCollection 2024.

ABSTRACT

PURPOSE: Only a few studies have focused on the brain mechanisms underlying the itch processing in AD patients, and a neural biomarker has never been studied in AD patients. We aimed to develop a deep learning model-based neural signature which can extract the relevant temporal dynamics, discriminate between AD and healthy control (HC), and between AD patients who responded well to acupuncture treatment and those who did not.

PATIENTS AND METHODS: We recruited 41 AD patients (22 male, age mean ± SD: 24.34 ± 5.29) and 40 HCs (20 male, age mean ± SD: 26.4 ± 5.32), and measured resting-state functional MRI signals. After preprocessing, 38 functional regions of interest were applied to the functional MRI signals. A long short-term memory (LSTM) was used to extract the relevant temporal dynamics for classification and train the prediction model. Bootstrapping and 4-fold cross-validation were used to examine the significance of the models.

RESULTS: For the identification of AD patients and HC, we found that the supplementary motor area (SMA), posterior cingulate cortex (PCC), temporal pole, precuneus, and dorsolateral prefrontal cortex showed significantly greater prediction accuracy than the chance level. For the identification of high and low responder to acupuncture treatment, we found that the lingual-parahippocampal-fusiform gyrus, SMA, frontal gyrus, PCC and precuneus, paracentral lobule, and primary motor and somatosensory cortex showed significantly greater prediction accuracy than the chance level.

CONCLUSION: We developed and evaluated a deep learning model-based neural biomarker that can distinguish between AD and HC as well as between AD patients who respond well and those who respond less to acupuncture. Using the intrinsic neurological abnormalities, it is possible to diagnose AD patients and provide personalized treatment regimens.

PMID:38651018 | PMC:PMC11034564 | DOI:10.2147/JAA.S454807

Sci Data. 2024 Apr 22;11(1):408. doi: 10.1038/s41597-024-03242-y.

ABSTRACT

Cocaine use disorder (CUD) is a global health problem with severe consequences, leading to behavioral, cognitive, and neurobiological disturbances. While consensus on treatments is still ongoing, repetitive transcranial magnetic stimulation (rTMS) has emerged as a promising approach for medication-resistant disorders, including substance use disorders. In this context, here we present the SUDMEX-TMS, a Mexican dataset from an rTMS clinical trial involving CUD patients. This longitudinal dataset comprises 54 CUD patients (including 8 females) with data collected at five time points: baseline (T0), two weeks (T1), three months (T2), six months (T3) follow-up, and twelve months (T4) follow-up. The clinical rTMS treatment followed a double-blinded randomized clinical trial design (n = 24 sham/30 active) for 2 weeks, followed by an open-label phase. The dataset includes demographic, clinical, and cognitive measures, as well as magnetic resonance imaging (MRI) data collected at all time points, encompassing structural (T1-weighted), functional (resting-state fMRI), and multishell diffusion-weighted (DWI-HARDI) sequences. This dataset offers the opportunity to investigate the impact of rTMS on CUD participants, considering clinical, cognitive, and multimodal MRI metrics in a longitudinal framework.

PMID:38649689 | DOI:10.1038/s41597-024-03242-y

Hum Brain Mapp. 2024 Apr 15;45(6):e26678. doi: 10.1002/hbm.26678.

ABSTRACT

Functional gradient (FG) analysis represents an increasingly popular methodological perspective for investigating brain hierarchical organization but whether and how network hierarchy changes concomitant with functional connectivity alterations in multiple sclerosis (MS) has remained elusive. Here, we analyzed FG components to uncover possible alterations in cortical hierarchy using resting-state functional MRI (rs-fMRI) data acquired in 122 MS patients and 97 healthy control (HC) subjects. Cortical hierarchy was assessed by deriving regional FG scores from rs-fMRI connectivity matrices using a functional parcellation of the cerebral cortex. The FG analysis identified a primary (visual-to-sensorimotor) and a secondary (sensory-to-transmodal) component. Results showed a significant alteration in cortical hierarchy as indexed by regional changes in FG scores in MS patients within the sensorimotor network and a compression (i.e., a reduced standard deviation across all cortical parcels) of the sensory-transmodal gradient axis, suggesting disrupted segregation between sensory and cognitive processing. Moreover, FG scores within limbic and default mode networks were significantly correlated ( ρ = 0.30 $$ \rho =0.30 $$ , p < .005 after Bonferroni correction for both) with the symbol digit modality test (SDMT) score, a measure of information processing speed commonly used in MS neuropsychological assessments. Finally, leveraging supervised machine learning, we tested the predictive value of network-level FG features, highlighting the prominent role of the FG scores within the default mode network in the accurate prediction of SDMT scores in MS patients (average mean absolute error of 1.22 ± 0.07 points on a hold-out set of 24 patients). Our work provides a comprehensive evaluation of FG alterations in MS, shedding light on the hierarchical organization of the MS brain and suggesting that FG connectivity analysis can be regarded as a valuable approach in rs-fMRI studies across different MS populations.

PMID:38647001 | PMC:PMC11033924 | DOI:10.1002/hbm.26678