Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain

Assessing Relative Linguistic Impairment With Model-Based Item Selection

PURPOSE

A picture naming test is presented that reveals impairment to specific mechanisms involved in the naming process, using accuracy scores on curated item sets. A series of psychometric validation experiments are reported.

METHOD

Using a computational model that enables estimation of item difficulty at the lexical and sublexical stages of word retrieval, two complimentary sets of items were constructed that challenge the respective psycholinguistic levels of representation. The difference in accuracy between these item sets yields the relative linguistic impairment (RLI) score. In a cohort of 91 people with chronic left-hemisphere stroke who enrolled in a clinical trial for anomia, we assessed psychometric properties of the RLI score and then used the new scale to make predictions about other language behaviors, lesion distributions, and functional activation during naming.

RESULTS

RLI scores had adequate psychometric properties for clinical significance. RLI scores contained predictive information about spontaneous speech fluency, over and above accuracy. A dissociation was observed between performance on the RLI item sets and performance on the subtests of an independent language battery. Sublexical RLI was significantly associated with apraxia of speech and with lesions encompassing perisylvian regions, while lexical RLI was associated with lesions to deep white matter. The RLI construct was reflected in functional brain activity during naming, independent of overall accuracy, with a respective shift of activation between dorsal and ventral networks responsible for different aspects of word retrieval.

CONCLUSION

The RLI assessment satisfies the psychometric requirements to serve as a useful clinical measure.

Regional brain activity and connectivity associated with childhood trauma in drug-naive patients with obsessive-compulsive disorder

Obsessive-compulsive disorder (OCD) is characterized by intrusive thoughts and repetitive, compulsive behaviors, with childhood trauma recognized as a contributing factor to its pathophysiology. This study aimed to delineate brain functional aberrations in OCD patients and explore the association between these abnormalities and childhood trauma, to gain insights into the neural underpinnings of OCD. Forty-eight drug-naive OCD patients and forty-two healthy controls (HC) underwent resting-state functional magnetic resonance imaging and clinical assessments, including the Yale-Brown Obsessive Compulsive Scale (Y-BOCS) and Childhood Trauma Questionnaire-Short Form (CTQ-SF). Compared to HCs, OCD patients exhibited significantly decreased amplitude of low-frequency fluctuations (ALFF) in the right cerebellum, decreased regional homogeneity (ReHo) in the right cerebellum and right superior occipital lobes (FWE-corrected p < 0.05), which negatively correlated with Y-BOCS scores (p < 0.05). Furthermore, cerebellar ALFF negatively correlated with the CTQ emotional abuse subscale (r = - 0.514, p < 0.01). Mediation analysis revealed that cerebellar ALFF mediated the relationship between CTQ-emotional abuse and Y-BOCS (good model fit: R2 = 0.231, MSE = 14.311, F = 5.721, p < 0.01; direct effect, c' = 0.153, indirect effect, a*b = 0.191). Findings indicated abnormal spontaneous and regional cerebellar activity in OCD, suggesting childhood trauma impacts OCD symptoms through cerebellar neural remodeling, highlighting its importance for clinical treatment selection.

Freezing of gait in Parkinson's disease is related to imbalanced stopping-related cortical activity

Freezing of gait, characterized by involuntary interruptions of walking, is a debilitating motor symptom of Parkinson's disease that restricts people's autonomy. Previous brain imaging studies investigating the mechanisms underlying freezing were restricted to scan people in supine positions and yielded conflicting theories regarding the role of the supplementary motor area and other cortical regions. We used functional near-infrared spectroscopy to investigate cortical haemodynamics related to freezing in freely moving people. We measured functional near-infrared spectroscopy activity over multiple motor-related cortical areas in 23 persons with Parkinson's disease who experienced daily freezing ('freezers') and 22 age-matched controls during freezing-provoking tasks including turning and doorway passing, voluntary stops and actual freezing. Crucially, we corrected the measured signals for confounds of walking. We first compared cortical activity between freezers and controls during freezing-provoking tasks without freezing (i.e. turning and doorway passing) and during stops. Secondly, within the freezers, we compared cortical activity between freezing, stopping and freezing-provoking tasks without freezing. First, we show that turning and doorway passing (without freezing) resemble cortical activity during stopping in both groups involving activation of the supplementary motor area and prefrontal cortex, areas known for their role in inhibiting actions. During these freezing-provoking tasks, the freezers displayed higher activity in the premotor areas than controls. Secondly, we show that, during actual freezing events, activity in the prefrontal cortex was lower than during voluntary stopping. The cortical relation between the freezing-provoking tasks (turning and doorway passing) and stopping may explain their susceptibility to trigger freezing by activating a stopping mechanism. Besides, the stopping-related activity of the supplementary motor area and prefrontal cortex seems to be out of balance in freezers. In this paper, we postulate that freezing results from a paroxysmal imbalance between the supplementary motor area and prefrontal cortex, thereby extending upon the current role of the supplementary motor area in freezing pathophysiology.

Early detection of dopaminergic dysfunction and glymphatic system impairment in Parkinson's disease

PURPOSE

This study aimed to assess the glymphatic function and its correlation with clinical characteristics and the loss of dopaminergic neurons in Parkinson's disease (PD) using hybrid positron emission tomography (PET)-magnetic resonance imaging (MRI) combined with diffusion tensor image analysis along the perivascular space (DTI-ALPS), choroid plexus volume (CPV), and enlarged perivascular space (EPVS) volume.

METHODS

Twenty-five PD patients and thirty matched healthy controls (HC) participated in the study. All participants underwent 18F-fluorodopa (18F-DOPA) PET-MRI scanning. The striatal standardized uptake value ratio (SUVR), DTI-ALPS index, CPV, and EPVS volume were calculated. Furthermore, we also analysed the relationship between the DTI-ALPS index, CPV, EPVS volume and striatal SUVR as well as clinical characteristics of PD patients.

RESULTS

PD patients demonstrated significantly lower values in DTI-ALPS (t = 3.053, p = 0.004) and larger CPV (t = 2.743, p = 0.008) and EPVS volume (t = 2.807, p = 0.008) compared to HC. In PD group, the ALPS-index was negatively correlated with the Unified Parkinson's Disease Rating Scale III (UPDRS-III) scores (r = -0.730, p < 0.001), and positively correlated with the mean putaminal SUVR (r = 0.560, p = 0.007) and mean caudal SUVR (r = 0.459, p = 0.032). Moreover, the mean putaminal SUVR was negatively associated with the UPDRS-III scores (r = -0.544, p = 0.009).

CONCLUSION

DTI-ALPS has the potential to uncover glymphatic dysfunction in patients with PD, with this dysfunction correlating strongly with the severity of disease, together with the mean putaminal and caudal SUVR. PET- MRI can serve as a potential multimodal imaging biomarker for early-stage PD.

Daily fat intake is associated with basolateral amygdala response to high-calorie food cues and appetite for high-calorie food

OBJECTIVES

Animal studies have indicated that fat intake mediates amygdala activation, which in turn promotes fat intake, while amygdala activation increases the preference for fat and leads to increased fat intake. However, the association among fat intake, amygdala activation, and appetite for high-calorie foods in humans remains unclear. Thus, to examine this association, we conducted a functional magnetic resonance imaging (fMRI) experiment.

METHODS

Fifty healthy-weight adults (18 females; mean age: 22.9 ± 3.02 years) were included. Participants were shown images of high-calorie and low-calorie foods and were instructed to rate their desire to eat the food items during fMRI. All participants provided information on their daily fat intake using a self-reported questionnaire. Associations among fat intake, the desire to eat high-calorie or low-calorie food items, and amygdala responses to food items were examined.

RESULTS

The basolateral amygdala (BLA) response was positively associated with fat intake ([x, y, z] = [24, -6, -16], z = 3.91, pFWE-corrected = 0.007) and the desire to eat high-calorie food items ([26, -4, -16], z = 3.75, pFWE-corrected = 0.010). Structural equation modeling showed that the desire for high-calorie food items was predicted by BLA response to high-calorie food items (p = 0.013, β = 3.176), and BLA response was predicted by fat intake (p < 0.001, β = 0.026).

DISCUSSION

Fat intake influences BLA response to high-fat food, which in turn increases the desire to eat palatable high-fat food. This may lead to additional fat intake and increase the risk of weight gain.

Sparse Graph Representation Learning Based on Reinforcement Learning for Personalized Mild Cognitive Impairment (MCI) Diagnosis

Resting-state functional magnetic resonance imaging (rs-fMRI) has gained attention as a reliable technique for investigating the intrinsic function patterns of the brain. It facilitates the extraction of functional connectivity networks (FCNs) that capture synchronized activity patterns among regions of interest (ROIs). Analyzing FCNs enables the identification of distinctive connectivity patterns associated with mild cognitive impairment (MCI). For MCI diagnosis, various sparse representation techniques have been introduced, including statistical- and deep learning-based methods. However, these methods face limitations due to their reliance on supervised learning schemes, which restrict the exploration necessary for probing novel solutions. To overcome such limitation, prior work has incorporated reinforcement learning (RL) to dynamically select ROIs, but effective exploration remains challenging due to the vast search space during training. To tackle this issue, in this study, we propose an advanced RL-based framework that utilizes a divide-and-conquer approach to decompose the FCN construction task into smaller sub-problems in a subject-specific manner, enabling efficient exploration under each sub-problem condition. Additionally, we leverage the learned value function to determine the sparsity level of FCNs, considering individual characteristics of FCNs. We validate the effectiveness of our proposed framework by demonstrating its superior performance in MCI diagnosis on publicly available cohort datasets.

Spatiotemporal whole-brain activity and functional connectivity of melodies recognition

Music is a non-verbal human language, built on logical, hierarchical structures, that offers excellent opportunities to explore how the brain processes complex spatiotemporal auditory sequences. Using the high temporal resolution of magnetoencephalography, we investigated the unfolding brain dynamics of 70 participants during the recognition of previously memorized musical sequences compared to novel sequences matched in terms of entropy and information content. Measures of both whole-brain activity and functional connectivity revealed a widespread brain network underlying the recognition of the memorized auditory sequences, which comprised primary auditory cortex, superior temporal gyrus, insula, frontal operculum, cingulate gyrus, orbitofrontal cortex, basal ganglia, thalamus, and hippocampus. Furthermore, while the auditory cortex responded mainly to the first tones of the sequences, the activity of higher-order brain areas such as the cingulate gyrus, frontal operculum, hippocampus, and orbitofrontal cortex largely increased over time during the recognition of the memorized versus novel musical sequences. In conclusion, using a wide range of analytical techniques spanning from decoding to functional connectivity and building on previous works, our study provided new insights into the spatiotemporal whole-brain mechanisms for conscious recognition of auditory sequences.

Reliability of energy landscape analysis of resting-state functional MRI data

Energy landscape analysis is a data-driven method to analyse multidimensional time series, including functional magnetic resonance imaging (fMRI) data. It has been shown to be a useful characterization of fMRI data in health and disease. It fits an Ising model to the data and captures the dynamics of the data as movement of a noisy ball constrained on the energy landscape derived from the estimated Ising model. In the present study, we examine test-retest reliability of the energy landscape analysis. To this end, we construct a permutation test that assesses whether or not indices characterizing the energy landscape are more consistent across different sets of scanning sessions from the same participant (i.e. within-participant reliability) than across different sets of sessions from different participants (i.e. between-participant reliability). We show that the energy landscape analysis has significantly higher within-participant than between-participant test-retest reliability with respect to four commonly used indices. We also show that a variational Bayesian method, which enables us to estimate energy landscapes tailored to each participant, displays comparable test-retest reliability to that using the conventional likelihood maximization method. The proposed methodology paves the way to perform individual-level energy landscape analysis for given data sets with a statistically controlled reliability.

Mapping the unique neural engagement in deaf individuals during picture, word, and sign language processing: fMRI study

Employing functional magnetic resonance imaging (fMRI) techniques, we conducted a comprehensive analysis of neural responses during sign language, picture, and word processing tasks in a cohort of 35 deaf participants and contrasted these responses with those of 35 hearing counterparts. Our voxel-based analysis unveiled distinct patterns of brain activation during language processing tasks. Deaf individuals exhibited robust bilateral activation in the superior temporal regions during sign language processing, signifying the profound neural adaptations associated with sign comprehension. Similarly, during picture processing, the deaf cohort displayed activation in the right angular, right calcarine, right middle temporal, and left angular gyrus regions, elucidating the neural dynamics engaged in visual processing tasks. Intriguingly, during word processing, the deaf group engaged the right insula and right fusiform gyrus, suggesting compensatory mechanisms at play during linguistic tasks. Notably, the control group failed to manifest additional or distinctive regions in any of the tasks when compared to the deaf cohort, underscoring the unique neural signatures within the deaf population. Multivariate Pattern Analysis (MVPA) of functional connectivity provided a more nuanced perspective on connectivity patterns across tasks. Deaf participants exhibited significant activation in a myriad of brain regions, including bilateral planum temporale (PT), postcentral gyrus, insula, and inferior frontal regions, among others. These findings underscore the intricate neural adaptations in response to auditory deprivation. Seed-based connectivity analysis, utilizing the PT as a seed region, revealed unique connectivity pattern across tasks. These connectivity dynamics provide valuable insights into the neural interplay associated with cross-modal plasticity.

High-Order line graphs of fMRI data in major depressive disorder

BACKGROUND

Resting-state functional magnetic resonance imaging (rs-fMRI) technology and the complex network theory can be used to elucidate the underlying mechanisms of brain diseases. The successful application of functional brain hypernetworks provides new perspectives for the diagnosis and evaluation of clinical brain diseases; however, many studies have not assessed the attribute information of hyperedges and could not retain the high-order topology of hypergraphs. In addition, the study of multi-scale and multi-layered organizational properties of the human brain can provide richer and more accurate data features for classification models of depression.

PURPOSE

This work aims to establish a more accurate classification framework for the diagnosis of major depressive disorder (MDD) using the high-order line graph algorithm. And accuracy, sensitivity, specificity, precision, F1 score are used to validate its classification performance.

METHODS

Based on rs-fMRI data from 38 MDD subjects and 28 controls, we constructed a human brain hypernetwork and introduced a line graph model, followed by the construction of a high-order line graph model. The topological properties under each order line graph were calculated to measure the classification performance of the model. Finally, intergroup features that showed significant differences under each order line graph model were fused, and a support vector machine classifier was constructed using multi-kernel learning. The Kolmogorov-Smirnov nonparametric permutation test was used as the feature selection method and the classification performance was measured with the leave-one-out cross-validation method.

RESULTS

The high-order line graph achieved a better classification performance compared with other traditional hypernetworks (accuracy = 92.42%, sensitivity = 92.86%, specificity = 92.11%, precision = 89.66%, F1 = 91.23%). Furthermore, the brain regions found in the present study have been previously shown to be associated with the pathology of depression.

CONCLUSIONS

This work validated the classification model based on the high-order line graph, which can better express the topological features of the hypernetwork by comprehensively considering the hyperedge information under different connection strengths, thereby significantly improving the classification accuracy of MDD. Therefore, this method has potential for use in the clinical diagnosis of MDD.

Unraveling multi-scale neuroimaging biomarkers and molecular foundations for schizophrenia: A combined multivariate pattern analysis and transcriptome-neuroimaging association study

AIMS

Schizophrenia is characterized by alterations in resting-state spontaneous brain activity; however, it remains uncertain whether variations at diverse spatial scales are capable of effectively distinguishing patients from healthy controls. Additionally, the genetic underpinnings of these alterations remain poorly elucidated. We aimed to address these questions in this study to gain better understanding of brain alterations and their underlying genetic factors in schizophrenia.

METHODS

A cohort of 103 individuals with diagnosed schizophrenia and 110 healthy controls underwent resting-state functional MRI scans. Spontaneous brain activity was assessed using the regional homogeneity (ReHo) metric at four spatial scales: voxel-level (Scale 1) and regional-level (Scales 2-4: 272, 53, 17 regions, respectively). For each spatial scale, multivariate pattern analysis was performed to classify schizophrenia patients from healthy controls, and a transcriptome-neuroimaging association analysis was performed to establish connections between gene expression data and ReHo alterations in schizophrenia.

RESULTS

The ReHo metrics at all spatial scales effectively discriminated schizophrenia from healthy controls. Scale 2 showed the highest classification accuracy at 84.6%, followed by Scale 1 (83.1%) and Scale 3 (78.5%), while Scale 4 exhibited the lowest accuracy (74.2%). Furthermore, the transcriptome-neuroimaging association analysis showed that there were not only shared but also unique enriched biological processes across the four spatial scales. These related biological processes were mainly linked to immune responses, inflammation, synaptic signaling, ion channels, cellular development, myelination, and transporter activity.

CONCLUSIONS

This study highlights the potential of multi-scale ReHo as a valuable neuroimaging biomarker in the diagnosis of schizophrenia. By elucidating the complex molecular basis underlying the ReHo alterations of this disorder, this study not only enhances our understanding of its pathophysiology, but also pave the way for future advancements in genetic diagnosis and treatment of schizophrenia.

Compressed representation of brain genetic transcription

The architecture of the brain is too complex to be intuitively surveyable without the use of compressed representations that project its variation into a compact, navigable space. The task is especially challenging with high-dimensional data, such as gene expression, where the joint complexity of anatomical and transcriptional patterns demands maximum compression. The established practice is to use standard principal component analysis (PCA), whose computational felicity is offset by limited expressivity, especially at great compression ratios. Employing whole-brain, voxel-wise Allen Brain Atlas transcription data, here we systematically compare compressed representations based on the most widely supported linear and non-linear methods-PCA, kernel PCA, non-negative matrix factorisation (NMF), t-stochastic neighbour embedding (t-SNE), uniform manifold approximation and projection (UMAP), and deep auto-encoding-quantifying reconstruction fidelity, anatomical coherence, and predictive utility across signalling, microstructural, and metabolic targets, drawn from large-scale open-source MRI and PET data. We show that deep auto-encoders yield superior representations across all metrics of performance and target domains, supporting their use as the reference standard for representing transcription patterns in the human brain.

A Multiview Brain Network Transformer Fusing Individualized Information for Autism Spectrum Disorder Diagnosis

Functional connectivity (FC) networks, built from analyses of resting-state magnetic resonance imaging (rs-fMRI), serve as efficacious biomarkers for identifying Autism Spectrum Disorders (ASD) patients. Given the neurobiological heterogeneity across individuals and the unique presentation of ASD symptoms, the fusion of individualized information into diagnosis becomes essential. However, this aspect is overlooked in most methods. Furthermore, the existing methods typically focus on studying direct pairwise connections between brain ROIs, while disregarding interactions between indirectly connected neighbors. To overcome above challenges, we build common FC and individualized FC by tangent pearson embedding (TP) and common orthogonal basis extraction (COBE) respectively, and present a novel multiview brain transformer (MBT) aimed at effectively fusing common and indivinformation of subjects. MBT is mainly constructed by transformer layers with diffusion kernel (DK), fusion quality-inspired weighting module (FQW), similarity loss and orthonormal clustering fusion readout module (OCFRead). DK transformer can incorporate higher-order random walk methods to capture wider interactions among indirectly connected brain regions. FQW promotes adaptive fusion of features between views, and similarity loss and OCFRead are placed on the last layer to accomplish the ultimate integration of information. In our method, TP, DK and FQW modules all help to model wider connectivity in the brain that make up for the shortcomings of traditional methods. We conducted experiments on the public ABIDE dataset based on AAL and CC200 respectively. Our framework has shown promising results, outperforming state-of-the-art methods on both templates. This suggests its potential as a valuable approach for clinical ASD diagnosis.

Acute hypernatremia increases functional connectivity of NaCl sensing regions in the human brain: An fMRI pilot study

Rodent studies demonstrated specialized sodium chloride (NaCl) sensing neurons in the circumventricular organs, which mediate changes in sympathetic nerve activity, arginine vasopressin, thirst, and blood pressure. However, the neural pathways involved in NaCl sensing in the human brain are incompletely understood. The purpose of this pilot study was to determine if acute hypernatremia alters the functional connectivity of NaCl-sensing regions of the brain in healthy young adults. Resting-state fMRI scans were acquired in 13 participants at baseline and during a 30 min hypertonic saline infusion (HSI). We used a seed-based approach to analyze the data, focusing on the subfornical organ (SFO) and the organum vasculosum of the lamina terminalis (OVLT) as regions of interest (ROIs). Blood chemistry and perceived thirst were assessed pre- and post-infusion. As expected, serum sodium increased from pre- to post-infusion in the HSI group. The primary finding of this pilot study was that the functional connectivity between the SFO and a cluster within the OVLT increased from baseline to the late-phase of the HSI. Bidirectional connectivity changes were found with cortical regions, with some regions showing increased connectivity with sodium-sensing regions while others showed decreased connectivity. Furthermore, the functional connectivity between the SFO and the posterior cingulate cortex (a control ROI) did not change from baseline to the late-phase of the HSI. This finding indicates a distinct response within the NaCl sensing network in the human brain specifically related to acute hypernatremia that will need to be replicated in large-scale studies.

A survey of brain functional network extraction methods using fMRI data

Functional network (FN) analyses play a pivotal role in uncovering insights into brain function and understanding the pathophysiology of various brain disorders. This paper focuses on classical and advanced methods for deriving brain FNs from functional magnetic resonance imaging (fMRI) data. We systematically review their foundational principles, advantages, shortcomings, and interrelations, encompassing both static and dynamic FN extraction approaches. In the context of static FN extraction, we present hypothesis-driven methods such as region of interest (ROI)-based approaches as well as data-driven methods including matrix decomposition, clustering, and deep learning. For dynamic FN extraction, both window-based and windowless methods are surveyed with respect to the estimation of time-varying FN and the subsequent computation of FN states. We also discuss the scope of application of the various methods and avenues for future improvements.

Brain imaging of a gamified cognitive flexibility task in young and older adults

The study aimed to develop and validate a gamified cognitive flexibility task through brain imaging, and to investigate behavioral and brain activation differences between young and older adults during task performance. Thirty-one young adults (aged 18-35) and 31 older adults (aged 60-80) were included in the present study. All participants underwent fMRI scans while completing the gamified cognitive flexibility task. Results showed that young adults outperformed older adults on the task. The left inferior frontal junction (IFJ), a key region of cognitive flexibility, was significantly activated during the task in both older and young adults. Comparatively, the percent signal change in the left IFJ was stronger in older adults than in young adults. Moreover, older adults demonstrated more precise representations during the task in the left IFJ. Additionally, the left inferior parietal lobule (IPL) and superior parietal lobule in older adults and the left middle frontal gyrus (MFG) and inferior frontal gyrus in young adults were also activated during the task. Psychophysiological interaction analyses showed significant functional connectivity between the left IFJ and the left IPL, as well as the right precuneus in older adults. In young adults, significant functional connectivity was found between the left IFJ and the left MFG, as well as the right angular. The current study provides preliminary evidence for the validity of the gamified cognitive flexibility task through brain imaging. The findings suggest that this task could serve as a reliable tool for assessing cognitive flexibility and for exploring age-related differences of cognitive flexibility in both brain and behavior.

Abnormal Dynamic Reconstruction of Overlapping Communities in Schizophrenia Patients

OBJECTIVE

This study aims to explore the changes in dynamic overlapping communities in the brains of schizophrenia (SZ) patients and further investigate the dynamic restructuring patterns of overlapping communities in SZ patients.

MATERIALS AND METHODS

A total of 43 SZ patients and 49 normal controls (NC) were selected for resting-state functional MRI (rs-fMRI) scans. Dynamic functional connectivity analysis was conducted separately on SZ patients and NC using rs-fMRI and Jackknife Correlation techniques to construct dynamic brain network models. Based on these models, a dynamic overlapping community detection method was utilized to explore the abnormal overlapping community structure in SZ patients using evaluation metrics such as the structural stability of overlapping communities, nodes' functional diversity, and activity level of overlapping communities.

RESULTS

The stability of communities in SZ patients showed a decreasing trend. The changes in the overlapping community structure of SZ patients may be related to a decrease in the diversity of overlapping node functions. Additionally, compared to the NC group, the activity level of overlapping communities of SZ patients was significantly reduced.

CONCLUSION

The structure or organization of the brain functional network in SZ patients is abnormal or disrupted, and the activity of the brain network in information processing and transmission is weakened in SZ patients.

Age-related differences in functional connectivity associated with pain modulation

Growing evidence suggests that aging is associated with impaired endogenous pain modulation, and that this likely underlies the increased transition from acute to chronic pain in older individuals. Resting-state functional connectivity (rsFC) offers a valuable tool to examine the neural mechanisms behind these age-related changes in pain modulation. RsFC studies generally observe decreased within-network connectivity due to aging, but its relevance for pain modulation remains unknown. We compared rsFC within a set of brain regions involved in pain modulation between young and older adults and explored the relationship with the efficacy of distraction from pain. This revealed several age-related increases and decreases in connectivity strength. Importantly, we found a significant association between lower pain relief and decreased strength of three connections in older adults, namely between the periaqueductal gray and right insula, between the anterior cingulate cortex (ACC) and right insula, and between the ACC and left amygdala. These findings suggest that the functional integrity of the pain control system is critical for effective pain modulation, and that its function is compromised by aging.

Validation and Evaluation of a Vendor-Provided Head Motion Correction Algorithm on the uMI Panorama PET/CT System

Brain PET imaging often faces challenges from head motion (HM), which can introduce artifacts and reduce image resolution, crucial in clinical settings for accurate treatment planning, diagnosis, and monitoring. United Imaging Healthcare has developed NeuroFocus, an HM correction (HMC) algorithm for the uMI Panorama PET/CT system, using a data-driven, statistics-based approach. The HMC algorithm automatically detects HM using a centroid-of-distribution technique, requiring no parameter adjustments. This study aimed to validate NeuroFocus and assess the prevalence of HM in clinical short-duration 18F-FDG scans. Methods: The study involved 317 patients undergoing brain PET scans, divided into 2 groups: 15 for HMC validation and 302 for evaluation. Validation involved patients undergoing 2 consecutive 3-min single-bed-position brain 18F-FDG scans-one with instructions to remain still and another with instructions to move substantially. The evaluation examined 302 clinical single-bed-position brain scans for patients with various neurologic diagnoses. Motion was categorized as small or large on the basis of a 5% SUV change in the frontal lobe after HMC. Percentage differences in SUVmean were reported across 11 brain regions. Results: The validation group displayed a large negative difference (-10.1%), with variation of 5.2% between no-HM and HM scans. After HMC, this difference decreased dramatically (-0.8%), with less variation (3.2%), indicating effective HMC application. In the evaluation group, 38 of 302 patients experienced large HM, showing a 10.9% ± 8.9% SUV increase after HMC, whereas most exhibited minimal uptake changes (0.1% ± 1.3%). The HMC algorithm not only enhanced the image resolution and contrast but also aided in disease identification and reduced the need for repeat scans, potentially optimizing clinical workflows. Conclusion: The study confirmed the effectiveness of NeuroFocus in managing HM in short clinical 18F-FDG studies on the uMI Panorama PET/CT system. It found that approximately 12% of scans required HMC, establishing HMC as a reliable tool for clinical brain 18F-FDG studies.

Prospective Longitudinal Perfusion in Probable Alzheimer's Disease Correlated with Atrophy in Temporal Lobe

Reduced cerebral blood flow (CBF) in the temporoparietal region and gray matter volumes (GMVs) in the temporal lobe were previously reported in patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD). However, the temporal relationship between reductions in CBF and GMVs requires further investigation. This study sought to determine if reduced CBF is associated with reduced GMVs, or vice versa. Data came from 148 volunteers of the Cardiovascular Health Study Cognition Study (CHS-CS), including 58 normal controls (NC), 50 MCI, and 40 AD who had perfusion and structural MRIs during 2002-2003 (Time 2). Sixty-three of the 148 volunteers had follow-up perfusion and structural MRIs (Time 3). Forty out of the 63 volunteers received prior structural MRIs during 1997-1999 (Time 1). The relationships between GMVs and subsequent CBF changes, and between CBF and subsequent GMV changes were investigated. At Time 2, we observed smaller GMVs (p<0.05) in the temporal pole region in AD compared to NC and MCI. We also found associations between: (1) temporal pole GMVs at Time 2 and subsequent declines in CBF in this region (p=0.0014) and in the temporoparietal region (p=0.0032); (2) hippocampal GMVs at Time 2 and subsequent declines in CBF in the temporoparietal region (p=0.012); and (3) temporal pole CBF at Time 2 and subsequent changes in GMV in this region (p = 0.011). Therefore, hypoperfusion in the temporal pole may be an early event driving its atrophy. Perfusion declines in the temporoparietal and temporal pole follow atrophy in this temporal pole region.

Latent diffusion model-based MRI superresolution enhances mild cognitive impairment prognostication and Alzheimer's disease classification

INTRODUCTION

Timely diagnosis and prognostication of Alzheimer's disease (AD) and mild cognitive impairment (MCI) are pivotal for effective intervention. Artificial intelligence (AI) in neuroradiology may aid in such appropriate diagnosis and prognostication. This study aimed to evaluate the potential of novel diffusion model-based AI for enhancing AD and MCI diagnosis through superresolution (SR) of brain magnetic resonance (MR) images.

METHODS

1.5T brain MR scans of patients with AD or MCI and healthy controls (NC) from Alzheimer's Disease Neuroimaging Initiative 1 (ADNI1) were superresolved to 3T using a novel diffusion model-based generative AI (d3T*) and a convolutional neural network-based model (c3T*). Comparisons of image quality to actual 1.5T and 3T MRI were conducted based on signal-to-noise ratio (SNR), naturalness image quality evaluator (NIQE), and Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE). Voxel-based volumetric analysis was then conducted to study whether 3T* images offered more accurate volumetry than 1.5T images. Binary and multiclass classifications of AD, MCI, and NC were conducted to evaluate whether 3T* images offered superior AD classification performance compared to actual 1.5T MRI. Moreover, CNN-based classifiers were used to predict conversion of MCI to AD, to evaluate the prognostication performance of 3T* images. The classification performances were evaluated using accuracy, sensitivity, specificity, F1 score, Matthews correlation coefficient (MCC), and area under the receiver-operating curves (AUROC).

RESULTS

Analysis of variance (ANOVA) detected significant differences in image quality among the 1.5T, c3T*, d3T*, and 3T groups across all metrics. Both c3T* and d3T* showed superior image quality compared to 1.5T MRI in NIQE and BRISQUE with statistical significance. While the hippocampal volumes measured in 3T* and 3T images were not significantly different, the hippocampal volume measured in 1.5T images showed significant difference. 3T*-based AD classifications showed superior performance across all performance metrics compared to 1.5T-based AD classification. Classification performance between d3T* and actual 3T was not significantly different. 3T* images offered superior accuracy in predicting the conversion of MCI to AD than 1.5T images did.

CONCLUSIONS

The diffusion model-based MRI SR enhances the resolution of brain MR images, significantly improving diagnostic and prognostic accuracy for AD and MCI. Superresolved 3T* images closely matched actual 3T MRIs in quality and volumetric accuracy, and notably improved the prediction performance of conversion from MCI to AD.

Population-level task-evoked functional connectivity via Fourier analysis

Functional magnetic resonance imaging (fMRI) is a noninvasive and in-vivo imaging technique essential for measuring brain activity. Functional connectivity is used to study associations between brain regions, either while study subjects perform tasks or during periods of rest. In this paper, we propose a rigorous definition of task-evoked functional connectivity at the population level (ptFC). Importantly, our proposed ptFC is interpretable in the context of task-fMRI studies. An algorithm for estimating the ptFC is provided. We present the performance of the proposed algorithm compared to existing functional connectivity frameworks using simulations. Lastly, we apply the proposed algorithm to estimate the ptFC in a motor-task study from the Human Connectome Project.

The White Matter Integrity and Functional Connection Differences of Fornix (Cres)/Stria Terminalis in Individuals with Mild Cognitive Impairment Induced by Occupational Aluminum Exposure

Long-term aluminum (Al) exposure increases the risk of mild cognitive impairment (MCI). The aim of the present study was to investigate the neural mechanisms of Al-induced MCI. In our study, a total of 52 individuals with occupational Al exposure >10 years were enrolled and divided into two groups: MCI (Al-MCI) and healthy controls (Al-HC). Plasma Al concentrations and Montreal Cognitive Assessment (MoCA) score were collected for all participants. And diffusion tensor imaging and resting-state functional magnetic resonance imaging were used to examine changes of white matter (WM) and functional connectivity (FC). There was a negative correlation between MoCA score and plasma Al concentration. Compared with the Al-HC, fractional anisotropy value for the right fornix (cres)/stria terminalis (FX/ST) was higher in the Al-MCI. Furthermore, there was a difference in FC between participants with and without MCI under Al exposure. We defined the regions with differing FC as a "pathway," specifically the connectivity from the right temporal pole to the right FX/ST, then to the right sagittal stratum, and further to the right anterior cingulate and paracingulate gyri and right inferior frontal gyrus, orbital part. In summary, we believe that the observed differences in WM integrity and FC in the right FX/ST between participants with and without MCI under long-term Al exposure may represent the neural mechanisms underlying MCI induced by Al exposure.

Longitudinal associations between language network characteristics in the infant brain and school-age reading abilities are mediated by early-developing phonological skills

Reading acquisition is a prolonged learning process relying on language development starting in utero. Behavioral longitudinal studies reveal prospective associations between infant language abilities and preschool/kindergarten phonological development that relates to subsequent reading performance. While recent pediatric neuroimaging work has begun to characterize the neural network underlying language development in infants, how this neural network scaffolds long-term language and reading acquisition remains unknown. We addressed this question in a 7-year longitudinal study from infancy to school-age. Seventy-six infants completed resting-state fMRI scanning, and underwent standardized language assessments in kindergarten. Of this larger cohort, forty-one were further assessed on their emergent word reading abilities after receiving formal reading instructions. Hierarchical clustering analyses identified a modular infant language network in which functional connectivity (FC) of the inferior frontal module prospectively correlated with kindergarten-age phonological skills and emergent word reading abilities. These correlations were obtained when controlling for infant age at scan, nonverbal IQ and parental education. Furthermore, kindergarten-age phonological skills mediated the relationship between infant FC and school-age reading abilities, implying a critical mid-way milestone for long-term reading development from infancy. Overall, our findings illuminate the neurobiological mechanisms by which infant language capacities could scaffold long-term reading acquisition.

A review of ADHD detection studies with machine learning methods using rsfMRI data

Attention deficit hyperactivity disorder (ADHD) is a common mental health condition that significantly affects school-age children, causing difficulties with learning and daily functioning. Early identification is crucial, and reliable and objective diagnostic tools are necessary. However, current clinical evaluations of behavioral symptoms can be inconsistent and subjective. Functional magnetic resonance imaging (fMRI) is a non-invasive technique that has proven effective in detecting brain abnormalities in individuals with ADHD. Recent studies have shown promising outcomes in using resting state fMRI (rsfMRI)-based brain functional networks to diagnose various brain disorders, including ADHD. Several review papers have examined the detection of other diseases using fMRI data and machine learning or deep learning methods. However, no review paper has specifically addressed ADHD. Therefore, this study aims to contribute to the literature by reviewing the use of rsfMRI data and machine learning methods for detection of ADHD. The study provides general information about fMRI databases and detailed knowledge of the ADHD-200 database, which is commonly used for ADHD detection. It also emphasizes the importance of examining all stages of the process, including network and atlas selection, feature extraction, and feature selection, before the classification stage. The study compares the performance, advantages, and disadvantages of previous studies in detail. This comprehensive approach may be a useful starting point for new researchers in this area.

Brain alterations in Cocaine Use Disorder: Does the route of use matter and does it relate to the treatment outcome?

AIMS

Cocaine Use Disorder (CUD) is an important health issue, associated with structural brain abnormalities. However, the impact of the route of administration and their predictive value for relapse remain unknown.

METHODS

We conducted an anatomical MRI study in 55 CUD patients (26 CUD-Crack and 29 CUD-Hydro) entering inpatient detoxification, and 38 matched healthy controls. In patients, a 3-months outpatient follow-up was carried out to specify the treatment outcome status (relapser when cocaine was consumed once or more during the past month). A Voxel-Based Morphometry approach was used.

RESULTS

Compared with controls, CUD patients had widespread gray matter alterations, mostly in frontal and temporal cortices, but also in the cerebellum and several sub-cortical structures. We then compared CUD-Crack with CUD-Hydro patients and found that crack-cocaine use was associated with lower volume in the right inferior and middle temporal gyri, and the right fusiform gyrus. Cerebellar vermis was smaller during detoxification in subsequent relapsers compared to three-months abstainers.

CONCLUSIONS

Patients with CUD display widespread cortical and subcortical brain shrinkage. Patients with preferential crack-cocaine use and subsequent relapsers showed specific gray matter volume deficits, suggesting that different patterns of cocaine use and different clinical outcome are associated with different brain macrostructure.

A multi-frequency approach of the altered functional connectome for autism spectrum disorder identification

Autism spectrum disorder stands as a multifaceted and heterogeneous neurodevelopmental condition. The utilization of functional magnetic resonance imaging to construct functional brain networks proves instrumental in comprehending the intricate interplay between brain activity and autism spectrum disorder, thereby elucidating the underlying pathogenesis at the cerebral level. Traditional functional brain networks, however, typically confine their examination to connectivity effects within a specific frequency band, disregarding potential connections among brain areas that span different frequency bands. To harness the full potential of interregional connections across diverse frequency bands within the brain, our study endeavors to develop a novel multi-frequency analysis method for constructing a comprehensive functional brain networks that incorporates multiple frequencies. Specifically, our approach involves the initial decomposition of functional magnetic resonance imaging into distinct frequency bands through wavelet transform. Subsequently, Pearson correlation is employed to generate corresponding functional brain networks and kernel for each frequency band. Finally, the classification was performed by a multi-kernel support vector machine, to preserve the connectivity effects within each band and the connectivity patterns shared among the different bands. Our proposed multi-frequency functional brain networks method yielded notable results, achieving an accuracy of 89.1%, a sensitivity of 86.67%, and an area under the curve of 0.942 in a publicly available autism spectrum disorder dataset.

Identifying gray matter alterations in Cushing's disease using machine learning: An interpretable approach

BACKGROUND

Cushing's Disease (CD) is a rare clinical syndrome characterized by excessive secretion of adrenocorticotrophic hormone, leading to significant functional and structural brain alterations as observed in Magnetic Resonance Imaging (MRI). While traditional statistical analysis has been widely employed to investigate these MRI changes in CD, it has lacked the ability to predict individual-level outcomes.

PURPOSE

To address this problem, this paper has proposed an interpretable machine learning (ML) framework, including model-level assessment, feature-level assessment, and biology-level assessment to ensure a comprehensive analysis based on structural MRI of CD.

METHODS

The ML framework has effectively identified the changes in brain regions in the stage of model-level assessment, verified the effectiveness of these altered brain regions to predict CD from normal controls in the stage of feature-level assessment, and carried out a correlation analysis between altered brain regions and clinical symptoms in the stage of biology-level assessment.

RESULTS

The experimental results of this study have demonstrated that the Insula, Fusiform gyrus, Superior frontal gyrus, Precuneus, and the opercular portion of the Inferior frontal gyrus of CD showed significant alterations in brain regions. Furthermore, our study has revealed significant correlations between clinical symptoms and the frontotemporal lobes, insulin, and olfactory cortex, which also have been confirmed by previous studies.

CONCLUSIONS

The ML framework proposed in this study exhibits exceptional potential in uncovering the intricate pathophysiological mechanisms underlying CD, with potential applicability in diagnosing other diseases.

Enhancing Major Depressive Disorder Diagnosis With Dynamic-Static Fusion Graph Neural Networks

Major Depressive Disorder (MDD) is a debilitating, complex mental condition with unclear mechanisms hindering diagnostic progress. Research links MDD to abnormal brain connectivity using functional magnetic resonance imaging (fMRI). Yet, existing fMRI-based MDD models suffer from limitations, including neglecting dynamic network traits, lacking interpretability, and struggling with small datasets. We present DSFGNN, a novel graph neural network framework addressing these issues for improved MDD diagnosis. DSFGNN employs a graph isomorphism encoder to model static and dynamic brain networks, achieving effective fusion of temporal and spatial information through a spatiotemporal attention mechanism, thereby enhancing interpretability. Furthermore, we incorporate a causal disentangling module and orthogonal regularization module to augment the model's expressiveness. We evaluate DSFGNN on the Rest-meta-MDD dataset, yielding superior results compared to the best baseline. Besides, extensive ablation studies and interpretability analysis confirm DSFGNN's effectiveness and potential for biomarker discovery.

Striatal GABA levels correlate with risk sensitivity in monetary loss

Background

Decision-making under risk is a common challenge. It is known that risk-taking behavior varies between contexts of reward and punishment, yet the mechanisms underlying this asymmetry in risk sensitivity remain unclear.

Methods

This study used a monetary task to investigate neurochemical mechanisms and brain dynamics underpinning risk sensitivity. Twenty-eight participants engaged in a task requiring selection of visual stimuli to maximize monetary gains and minimize monetary losses. We modeled participant trial-and-error processes using reinforcement learning.

Results

Participants with higher subjective utility parameters showed risk preference in the gain domain (r = -0.59) and risk avoidance in the loss domain (r = -0.77). Magnetic resonance spectroscopy (MRS) revealed that risk avoidance in the loss domain was associated with γ-aminobutyric acid (GABA) levels in the ventral striatum (r = -0.42), but not in the insula (r = -0.15). Using functional magnetic resonance imaging (fMRI), we tested whether risk-sensitive brain dynamics contribute to participant risky choices. Energy landscape analyses demonstrated that higher switching rates between brain states, including the striatum and insula, were correlated with risk avoidance in the loss domain (r = -0.59), a relationship not observed in the gain domain (r = -0.02).

Conclusions

These findings from MRS and fMRI suggest that distinct mechanisms are involved in gain/loss decision making, mediated by subcortical neurometabolite levels and brain dynamic transitions.

Understanding the complex interplay between tau, amyloid and the network in the spatiotemporal progression of Alzheimer's Disease

INTRODUCTION

The interaction of amyloid and tau in neurodegenerative diseases is a central feature of AD pathophysiology. While experimental studies point to various interaction mechanisms, their causal direction and mode (local, remote or network-mediated) remain unknown in human subjects. The aim of this study was to compare mathematical reaction-diffusion models encoding distinct cross-species couplings to identify which interactions were key to model success.

METHODS

We tested competing mathematical models of network spread, aggregation, and amyloid-tau interactions on publicly available data from ADNI.

RESULTS

Although network spread models captured the spatiotemporal evolution of tau and amyloid in human subjects, the model including a one-way amyloid-to-tau aggregation interaction performed best.

DISCUSSION

This mathematical exposition of the "pas de deux" of co-evolving proteins provides quantitative, whole-brain support to the concept of amyloid-facilitated-tauopathy rather than the classic amyloid-cascade or pure-tau hypotheses, and helps explain certain known but poorly understood aspects of AD.

Reorganization of brain connectivity across the spectrum of clinical cognitive decline

Clinical cognitive decline, leading to Alzheimer's Disease Dementia (ADD), has long been interpreted as a disconnection syndrome, hindering the information flow capacity of the brain, hence leading to the well-known symptoms of ADD. The structural and functional brain connectome analyses play a central role in studies of brain from this perspective. However, most current research implicitly assumes that the changes accompanying the progression of cognitive decline are monotonous in time, whether measured across the entire brain or in fixed cortical regions. We investigate the structural and functional connectivity-wise reorganization of the brain without such assumptions across the entire spectrum. We utilize nodal assortativity as a local topological measure of connectivity and follow a data-centric approach to identify and verify relevant local regions, as well as to understand the nature of underlying reorganization. The analysis of our preliminary experimental data points to statistically significant, hyper and hypo-assortativity regions that depend on the disease's stage, and differ for structural and functional connectomes. Our results suggest a new perspective into the dynamic, potentially a mix of degenerative and compensatory, topological alterations that occur in the brain as cognitive decline progresses.

Neighborhood structure-guided brain functional networks estimation for mild cognitive impairment identification

The adoption and growth of functional magnetic resonance imaging (fMRI) technology, especially through the use of Pearson's correlation (PC) for constructing brain functional networks (BFN), has significantly advanced brain disease diagnostics by uncovering the brain's operational mechanisms and offering biomarkers for early detection. However, the PC always tends to make for a dense BFN, which violates the biological prior. Therefore, in practice, researchers use hard-threshold to remove weak connection edges or introduce l 1-norm as a regularization term to obtain sparse BFNs. However, these approaches neglect the spatial neighborhood information between regions of interest (ROIs), and ROI with closer distances has higher connectivity prospects than ROI with farther distances due to the principle of simple wiring costs in resent studies. Thus, we propose a neighborhood structure-guided BFN estimation method in this article. In detail, we figure the ROIs' Euclidean distances and sort them. Then, we apply the K-nearest neighbor (KNN) to find out the top K neighbors closest to the current ROIs, where each ROI's K neighbors are independent of each other. We establish the connection relationship between the ROIs and these K neighbors and construct the global topology adjacency matrix according to the binary network. Connect ROI nodes with k nearest neighbors using edges to generate an adjacency graph, forming an adjacency matrix. Based on adjacency matrix, PC calculates the correlation coefficient between ROIs connected by edges, and generates the BFN. With the purpose of evaluating the performance of the introduced method, we utilize the estimated BFN for distinguishing individuals with mild cognitive impairment (MCI) from the healthy ones. Experimental outcomes imply this method attains better classification performance than the baselines. Additionally, we compared it with the most commonly used time series methods in deep learning. Results of the performance of K-nearest neighbor-Pearson's correlation (K-PC) has some advantage over deep learning.

Subjective well-being can be predicted by caudate volume and promotion focus

It is well-known that the caudate nucleus is associated with motivational behaviors and subjective well-being. However, no longitudinal studies have examined the relationship between brain structure, behavioral orientations, and subjective well-being. This study analyzes data from our previous longitudinal study to examine whether future subjective well-being can be predicted by the volume of the caudate nucleus. We also examined whether behavioral orientation, based on the regulatory focus theory showing two orientations-promotion and prevention focus-was related to the volume of the caudate nucleus. Voxel-based morphometry analysis indicated that the left caudate volume was positively associated with rating scores for future subjective well-being and promotion orientation. Further, mediation analysis indicated that promotion orientation significantly mediated the relationship between future subjective well-being and left caudate volume. The findings indicate that future subjective well-being can be predicted by the volume of the left caudate nucleus, and that this relationship is mediated by promotion focus orientation.

Unilateral tactile agnosia as an onset symptom of corticobasal syndrome

Tactile agnosia is the inability to recognize objects via haptic exploration, in the absence of an elementary sensory deficit. Traditionally, it has been described as a disturbance in extracting information about the physical properties of objects ("apperceptive agnosia") or in associating object representation with its semantic meaning ("associative agnosia"). However, tactile agnosia is a rare and difficult-to-diagnose condition, due to the frequent co-occurrence of sensorimotor symptoms and the lack of consensus on the terminology and assessment methods. Among tactile agnosia classifications, hyloagnosia (i.e., difficulty in quality discrimination of objects) and morphoagnosia (i.e., difficulty in shape and size recognition) have been proposed to account for the apperceptive level. However, a dissociation between the two has been reported in two cases only. Indeed, very few cases of pure tactile agnosia have been described, mostly associated with vascular damages in somatosensory areas, in pre- and postcentral gyrus, intraparietal sulcus, supramarginal gyrus, and insular cortex. An open question is whether degenerative conditions affecting the same areas could lead to similar impairments. Here, we present a single case of unilateral right-hand tactile agnosia, in the context of corticobasal syndrome (CBS), a rare neurodegenerative disease. The patient, a 55-year-old woman, initially presented with difficulties in tactile object recognition, apraxia for the right hand, and an otherwise intact cognitive profile. At the neuroimaging level, she showed a lesion outcome of a right parietal oligodendroglioma removal and a left frontoparietal atrophy. We performed an experimental evaluation of tactile agnosia, targeting every level of tactile processing, from elementary to higher order tactile recognition processes. We also tested 18 healthy participants as a matched control sample. The patient showed intact tactile sensitivity and mostly intact hylognosis functions. Conversely, she was impaired with the right hand in exploring geometrical and meaningless shapes. The patient's clinical evolution in the following 3 years became consistent with the diagnosis of CBS and unilateral tactile apperceptive agnosia as the primary symptom onset in the absence of a cognitive decline. This is the third case described in the literature manifesting morphoagnosia with almost completely preserved hylognosis abilities and the first description of such dissociation in a case with CBS.

Hippocampus and olfactory impairment in Parkinson disease: a comparative exploratory combined volumetric/functional MRI study

INTRODUCTION

Patients with Parkinson's Disease (PD) commonly experience Olfactory Dysfunction (OD). Our exploratory study examined hippocampal volumetric and resting-state functional magnetic resonance imaging (rs-fMRI) variations in a Healthy Control (HC) group versus a cognitively normal PD group, further categorized into PD with No/Mild Hyposmia (PD-N/MH) and PD with Severe Hyposmia (PD-SH).

METHODS

We calculated participants' relative Total Hippocampal Volume (rTHV) and performed Spearman's partial correlations, controlled for age and gender, to examine the correlation between rTHV and olfactory performance assessed by the Odor Stick Identification Test for the Japanese (OSIT-J) score. Mann-Whitney U tests assessed rTHV differences across groups and subgroups, rejecting the null hypothesis for p < 0.05. Furthermore, a seed-based rs-fMRI analysis compared hippocampal connectivity differences using a one-way ANCOVA covariate model with controls for age and gender.

RESULTS

Spearman's partial correlations indicated a moderate positive correlation between rTHV and OSIT-J in the whole study population (ρ = 0.406; p = 0.007), PD group (ρ = 0.493; p = 0.008), and PD-N/MH subgroup (ρ = 0.617; p = 0.025). Mann-Whitney U tests demonstrated lower rTHV in PD-SH subgroup compared to both HC group (p = 0.013) and PD-N/MH subgroup (p = 0.029). Seed-to-voxel rsfMRI analysis revealed reduced hippocampal connectivity in PD-SH subjects compared to HC subjects with a single cluster of voxels.

CONCLUSIONS

Although the design of the study do not allow to make firm conclusions, it is reasonable to speculate that the progressive involvement of the hippocampus in PD patients is associated with the progression of OD.

Triple network resting-state functional connectivity patterns of alcohol heavy drinking

AIMS

Previous neuroimaging research in alcohol use disorder (AUD) has found altered functional connectivity in the brain's salience, default mode, and central executive (CEN) networks (i.e. the triple network model), though their specific associations with AUD severity and heavy drinking remains unclear. This study utilized resting-state fMRI to examine functional connectivity in these networks and measures of alcohol misuse.

METHODS

Seventy-six adult heavy drinkers completed a 7-min resting-state functional MRI scan during visual fixation. Linear regression models tested if connectivity in the three target networks was associated with past 12-month AUD symptoms and number of heavy drinking days in the past 30 days. Exploratory analyses examined correlations between connectivity clusters and impulsivity and psychopathology measures.

RESULTS

Functional connectivity within the CEN network (right and left lateral prefrontal cortex [LPFC] seeds co-activating with 13 and 15 clusters, respectively) was significantly associated with AUD symptoms (right LPFC: β = .337, p-FDR = .016; left LPFC: β = .291, p-FDR = .028) but not heavy drinking (p-FDR > .749). Post-hoc tests revealed six clusters co-activating with the CEN network were associated with AUD symptoms-right middle frontal gyrus, right inferior parietal gyrus, left middle temporal gyrus, and left and right cerebellum. Neither the default mode nor the salience network was significantly associated with alcohol variables. Connectivity in the left LPFC was correlated with monetary delay discounting (r = .25, p = .03).

CONCLUSIONS

These findings support previous associations between connectivity within the CEN network and AUD severity, providing additional specificity to the relevance of the triple network model to AUD.

Multivariate pattern analysis of medical imaging-based Alzheimer's disease

Alzheimer's disease (AD) is a devastating brain disorder that steadily worsens over time. It is marked by a relentless decline in memory and cognitive abilities. As the disease progresses, it leads to a significant loss of mental function. Early detection of AD is essential to starting treatments that can mitigate the progression of this disease and enhance patients' quality of life. This study aims to observe AD's brain functional connectivity pattern to extract essential patterns through multivariate pattern analysis (MVPA) and analyze activity patterns across multiple brain voxels. The optimized feature extraction techniques are used to obtain the important features for performing the training on the models using several hybrid machine learning classifiers for performing binary classification and multi-class classification. The proposed approach using hybrid machine learning classification has been applied to two public datasets named the Open Access Series of Imaging Studies (OASIS) and the AD Neuroimaging Initiative (ADNI). The results are evaluated using performance metrics, and comparisons have been made to differentiate between different stages of AD using visualization tools.

Changes on Cognition and Brain Network Temporal Variability After Pediatric Neurosurgery

BACKGROUND AND OBJECTIVES

Pediatric intracranial space-occupying lesions are common, with prognoses improving markedly in recent years, significantly extending survival. As such, there is an imperative to pay increased attention to the postoperative cognitive functions and brain network alterations in these children because these factors significantly influence their quality of life. Temporal variability (TV) analysis of brain networks captures the full extent of resting-state activities, reflecting cognitive functions and rehabilitation potential. However, previous research rarely uses TV analyses and most focus on adults or children after multidisciplinary treatments, not reflecting the combined effect caused by neurosurgery only and self-repair. This study gives our insights into this field from a holistic perspective.

METHODS

We studied 35 children with intracranial space-occupying lesions, analyzing pre- and postsurgery MRI and cognitive tests. We used TV analysis to assess changes and correlated imaging indicators with cognitive performance.

RESULTS

We observed a tendency for cognitive recovery after about 3 months postsurgery, primarily in the domains of social cognition and nonverbal reasoning. TV analysis of brain networks indicated increased nodal variability within systems such as the visual and sensorimotor networks, which are integral to external interactions. Correlative analysis showed that alterations in certain occipital regions were associated with changes in social cognition and nonverbal reasoning.

CONCLUSION

These findings suggest significant intrinsic repair in cognitive functions and brain networks at around 3 months postneurosurgery in children. This study not only enriches our comprehension of postoperative cognitive and brain network self-repair processes in children but also furnishes potential therapeutic targets for rehabilitation interventions and establishes a theoretical foundation for proactive surgical interventions.

Adverse childhood experiences, brain efficiency, and the development of pain symptoms in youth

BACKGROUND

Adverse childhood experiences (ACEs) are often reported by youths with chronic pain, and both ACEs and chronic pain disrupt how information is processed. However, it is unknown whether changes to shared neural networks underlie the relationship between ACEs and the development of pain symptoms. This study explored the relationships between ACEs, brain efficiency, and pain symptomology in youth.

METHODS

A community sample of youths aged 14-18 years underwent MRIs, answered trauma and pain questionnaires, and underwent pain sensory testing, twice, 3 months apart (Nbaseline = 44; Nfollow-up = 42). Sensory testing determined thresholds for mechanical and thermal stimuli. Global and local network efficiencies were evaluated using graph theory. Generalized estimating equations were applied to examine whether brain efficiency moderated the relationships between ACEs, pain intensity, and pain sensitivity (i.e., mechanical detection, heat pain, and temperature change thresholds).

RESULTS

There was a significant interaction between ACEs and global brain efficiency in association with pain intensity (β = -0.31, p = 0.02) and heat pain (β = -0.29, p = 0.004). Lower global brain efficiency exacerbated the relationship between ACEs and pain intensity (θX → Y|W = -1.16 = 0.37, p = 0.05), and heat pain sensitivity (θX → Y|W = -1.30 = 0.44, p = 0.05). Higher global brain efficiency ameliorated the relationship between ACEs and pain intensity (θX → Y|W = 1.75 = -0.53, p = 0.05).

CONCLUSIONS

The relationship between ACEs and pain symptomology was comparable to chronic pain phenotypes (i.e., higher pain intensity and pain thresholds) and may vary as a function of brain efficiency in youth. This stresses the importance of assessing for pain symptoms in trauma-exposed youth, as earlier identification and intervention are critical in preventing the chronification of pain.

SIGNIFICANCE

This article explores the relationship between ACEs, pain symptomology, and brain efficiency in youth. ACEs may affect how the brain processes information, including pain. Youths with lower brain efficiencies that were exposed to more ACEs have pain symptomology comparable to youths with chronic pain. Understanding this relationship is important for the earlier identification of pain symptoms, particularly in vulnerable populations such as youths exposed to trauma, and is critical for preventing the chronification of pain.

Patterns of brain volume and metabolism predict clinical features in the progressive supranuclear palsy spectrum

Progressive supranuclear palsy (PSP) is a neurodegenerative tauopathy that presents with highly heterogenous clinical syndromes. We perform cross-sectional data-driven discovery of independent patterns of brain atrophy and hypometabolism across the entire PSP spectrum. We then use these patterns to predict specific clinical features and to assess their relationship to phenotypic heterogeneity. We included 111 patients with PSP (60 with Richardson syndrome and 51 with cortical and subcortical variant subtypes). Ninety-one were used as the training set and 20 as a test set. The presence and severity of granular clinical variables such as postural instability, parkinsonism, apraxia and supranuclear gaze palsy were noted. Domains of akinesia, ocular motor impairment, postural instability and cognitive dysfunction as defined by the Movement Disorders Society criteria for PSP were also recorded. Non-negative matrix factorization was used on cross-sectional MRI and fluorodeoxyglucose-positron emission tomography (FDG-PET) scans. Independent models for each as well as a combined model for MRI and FDG-PET were developed and used to predict the granular clinical variables. Both MRI and FDG-PET were better at predicting presence of a symptom than severity, suggesting identification of disease state may be more robust than disease stage. FDG-PET predicted predominantly cortical abnormalities better than MRI such as ideomotor apraxia, apraxia of speech and frontal dysexecutive syndrome. MRI demonstrated prediction of cortical and more so sub-cortical abnormalities, such as parkinsonism. Distinct neuroanatomical foci were predictive in MRI- and FDG-PET-based models. For example, vertical gaze palsy was predicted by midbrain atrophy on MRI, but frontal eye field hypometabolism on FDG-PET. Findings also differed by scale or instrument used. For example, prediction of ocular motor abnormalities using the PSP Saccadic Impairment Scale was stronger than with the Movement Disorders Society Diagnostic criteria for PSP oculomotor impairment designation. Combination of MRI and FDG-PET demonstrated enhanced detection of parkinsonism and frontal syndrome presence and apraxia, cognitive impairment and bradykinesia severity. Both MRI and FDG-PET patterns were able to predict some measures in the test set; however, prediction of global cognition measured by Montreal Cognitive Assessment was the strongest. MRI predictions generalized more robustly to the test set. PSP leads to neurodegeneration in motor, cognitive and ocular motor networks at cortical and subcortical foci, leading to diverse yet overlapping clinical syndromes. To advance understanding of phenotypic heterogeneity in PSP, it is essential to consider data-driven approaches to clinical neuroimaging analyses.

Causal Roles of Ventral and Dorsal Neural Systems for Automatic and Control Self-Reference Processing: A Function Lesion Mapping Study

Background: Humans perceive and interpret the world through the lens of self-reference processes, typically facilitating enhanced performance for the task at hand. However, this research has predominantly emphasized the automatic facet of self-reference processing, overlooking how it interacts with control processes affecting everyday situations. Methods: We investigated this relationship between automatic and control self-reference processing in neuropsychological patients performing self-face perception tasks and the Birmingham frontal task measuring executive functions. Results: Principal component analysis across tasks revealed two components: one loaded on familiarity/orientation judgments reflecting automatic self-reference processing, and the other linked to the cross task and executive function indicating control processing requirements. Voxel-based morphometry and track-wise lesion-mapping analyses showed that impairments in automatic self-reference were associated with reduced grey matter in the ventromedial prefrontal cortex and right inferior temporal gyrus, and white matter damage in the right inferior fronto-occipital fasciculus. Deficits in executive control were linked to reduced grey matter in the bilateral inferior parietal lobule and left anterior insula, and white matter disconnections in the left superior longitudinal fasciculus and arcuate fasciculus. Conclusions: The causal evidence suggests that automatic and control facets of self-reference processes are subserved by distinct yet integrated ventral prefrontal-temporal and dorsal frontal-parietal networks, respectively.

Self-Regulation of the Posterior-Frontal Brain Activity with Real-Time fMRI Neurofeedback to Influence Perceptual Discrimination

The Global Neuronal Workspace (GNW) hypothesis states that the visual percept is available to conscious awareness only if recurrent long-distance interactions among distributed brain regions activate neural circuitry extending from the posterior areas to prefrontal regions above a certain excitation threshold. To directly test this hypothesis, we trained 14 human participants to increase blood oxygenation level-dependent (BOLD) signals with real-time functional magnetic resonance imaging (rtfMRI)-based neurofeedback simultaneously in four specific regions of the occipital, temporal, insular and prefrontal parts of the brain. Specifically, we hypothesized that the up-regulation of the mean BOLD activity in the posterior-frontal brain regions lowers the perceptual threshold for visual stimuli, while down-regulation raises the threshold. Our results showed that participants could perform up-regulation (Wilcoxon test, session 1: p = 0.022; session 4: p = 0.041) of the posterior-frontal brain activity, but not down-regulation. Furthermore, the up-regulation training led to a significant reduction in the visual perceptual threshold, but no substantial change in perceptual threshold was observed after the down-regulation training. These findings show that the up-regulation of the posterior-frontal regions improves the perceptual discrimination of the stimuli. However, further questions as to whether the posterior-frontal regions can be down-regulated at all, and whether down-regulation raises the perceptual threshold, remain unanswered.

Dopaminergic dysfunction in the left putamen of patients with major depressive disorder

INTRODUCTION

Dopaminergic transmission impairment has been identified as one of the main neurobiological correlates of both depression and clinical symptoms commonly associated with its spectrum such as anhedonia and psychomotor retardation.

OBJECTIVES

We examined the relationship between dopaminergic deficit in the striatum, as measured by 123I-FP-CIT SPECT imaging, and specific psychopathological dimensions in patients with major depressive disorder.

METHODS

To our knowledge this is the first study with a sample of >120 subjects. After check for inclusion and exclusion criteria, 121 (67 females, 54 males) patients were chosen retrospectively from an extensive 1106 patients database of 123I-FP-CIT SPECT scans obtained at the Nuclear Medicine Unit of Fondazione Policlinico Universitario Agostino Gemelli IRCCS in Rome. These individuals had undergone striatal dopamine transporter (DAT) assessments based on the recommendation of their referring clinicians, who were either neurologists or psychiatrists. At the time of SPECT imaging, each participant underwent psychiatric and psychometric evaluations. We used the following psychometric scales: Hamilton Depression Rating Scale, Hamilton Anxiety Rating Scale, Snaith Hamilton Pleasure Scale, and Depression Retardation Rating Scale.

RESULTS

We found a negative correlation between levels of depression (p = 0.007), anxiety (p = 0.035), anhedonia (p = 0.028) and psychomotor retardation (p = 0.014) and DAT availability in the left putamen. We further stratified the sample and found that DAT availability in the left putamen was lower in seriously depressed patients (p = 0.027) and in patients with significant psychomotor retardation (p = 0.048).

CONCLUSION

To our knowledge this is the first study to have such a high number of sample. Our study reveals a pivotal role of dopaminergic dysfunction in patients with major depressive disorder. Elevated levels of depression, anxiety, anhedonia, and psychomotor retardation appear to be associated with reduced DAT availability specifically in the left putamen.

Early nutritional influences on brain regions related to processing speed in children born preterm: A secondary analysis of a randomized trial

BACKGROUND

Processing speed is a foundational skill supporting intelligence and executive function, areas often delayed in preterm-born children. The impact of early-life nutrition on gray matter facilitating processing speed for this vulnerable population is unknown.

METHODS

Magnetic resonance imaging and the Wechsler Preschool and Primary Scale of Intelligence-IV Processing Speed Index were acquired in forty 5-year-old children born preterm with very low birth weight. Macronutrient (grams per kilogram per day) and mother's milk (percentage of feeds) intakes were prospectively collected in the first postnatal month and associations between early-life nutrition and the primary outcome of brain regions supporting processing speed were investigated.

RESULTS

Children had a mean (SD) gestational age of 27.8 (1.8) weeks and 45% were male. Macronutrient intakes were unrelated, but mother's milk was positively related, to greater volumes in brain regions, including total cortical gray matter, cingulate gyri, and occipital gyri.

CONCLUSION

First postnatal month macronutrient intakes showed no association, but mother's milk was positively associated, with volumetric measures of total and regional cortical gray matter related to processing speed in preterm-born children. This exploratory analysis suggests early-life mother's milk supports processing speed by impacting structural underpinnings. Further research is needed on this potential strategy to improve preterm outcomes.

Awareness of embodiment enhances enjoyment and engages sensorimotor cortices

Whether in performing arts, sporting, or everyday contexts, when we watch others move, we tend to enjoy bodies moving in synchrony. Our enjoyment of body movements is further enhanced by our own prior experience with performing those movements, or our 'embodied experience'. The relationships between movement synchrony and enjoyment, as well as embodied experience and movement enjoyment, are well known. The interaction between enjoyment of movements, synchrony, and embodiment is less well understood, and may be central for developing new approaches for enriching social interaction. To examine the interplay between movement enjoyment, synchrony, and embodiment, we asked participants to copy another person's movements as accurately as possible, thereby gaining embodied experience of movement sequences. Participants then viewed other dyads performing the same or different sequences synchronously, and we assessed participants' recognition of having performed these sequences, as well as their enjoyment of each movement sequence. We used functional near-infrared spectroscopy to measure cortical activation over frontotemporal sensorimotor regions while participants performed and viewed movements. We found that enjoyment was greatest when participants had mirrored the sequence and recognised it, suggesting that awareness of embodiment may be central to enjoyment of synchronous movements. Exploratory analyses of relationships between cortical activation and enjoyment and recognition implicated the sensorimotor cortices, which subserve action observation and aesthetic processing. These findings hold implications for clinical research and therapies seeking to foster successful social interaction.

Generative whole-brain dynamics models from healthy subjects predict functional alterations in stroke at the level of individual patients

Computational whole-brain models describe the resting activity of each brain region based on a local model, inter-regional functional interactions, and a structural connectome that specifies the strength of inter-regional connections. Strokes damage the healthy structural connectome that forms the backbone of these models and produce large alterations in inter-regional functional interactions. These interactions are typically measured by correlating the time series of the activity between two brain regions in a process, called resting functional connectivity. We show that adding information about the structural disconnections produced by a patient's lesion to a whole-brain model previously trained on structural and functional data from a large cohort of healthy subjects enables the prediction of the resting functional connectivity of the patient and fits the model directly to the patient's data (Pearson correlation = 0.37; mean square error = 0.005). Furthermore, the model dynamics reproduce functional connectivity-based measures that are typically abnormal in stroke patients and measures that specifically isolate these abnormalities. Therefore, although whole-brain models typically involve a large number of free parameters, the results show that, even after fixing those parameters, the model reproduces results from a population very different than that on which the model was trained. In addition to validating the model, these results show that the model mechanistically captures the relationships between the anatomical structure and the functional activity of the human brain.

Spectral-switching analysis reveals real-time neuronal network representations of concurrent spontaneous naturalistic behaviors in human brain

Despite abundant evidence of functional networks in the human brain, their neuronal underpinnings, and relationships to real-time behavior have been challenging to resolve. Analyzing brain-wide intracranial-EEG recordings with video monitoring, acquired in awake subjects during clinical epilepsy evaluation, we discovered the tendency of each brain region to switch back and forth between 2 distinct power spectral densities (PSDs 2-55Hz). We further recognized that this 'spectral switching' occurs synchronously between distant sites, even between regions with differing baseline PSDs, revealing long-range functional networks that would be obscured in analysis of individual frequency bands. Moreover, the real-time PSD-switching dynamics of specific networks exhibited striking alignment with activities such as conversation and hand movements, revealing a multi-threaded functional network representation of concurrent naturalistic behaviors. Network structures and their relationships to behaviors were stable across days, but were altered during N3 sleep. Our results provide a new framework for understanding real-time, brain-wide neural-network dynamics.

A new transfer entropy method for measuring directed connectivity from complex-valued fMRI data

Background

Inferring directional connectivity of brain regions from functional magnetic resonance imaging (fMRI) data has been shown to provide additional insights into predicting mental disorders such as schizophrenia. However, existing research has focused on the magnitude data from complex-valued fMRI data without considering the informative phase data, thus ignoring potentially important information.

Methods

We propose a new complex-valued transfer entropy (CTE) method to measure causal links among brain regions in complex-valued fMRI data. We use the transfer entropy to model a general non-linear magnitude-magnitude and phase-phase directed connectivity and utilize partial transfer entropy to measure the complementary phase and magnitude effects on magnitude-phase and phase-magnitude causality. We also define the significance of the causality based on a statistical test and the shuffling strategy of the two complex-valued signals.

Results

Simulated results verified higher accuracy of CTE than four causal analysis methods, including a simplified complex-valued approach and three real-valued approaches. Using experimental fMRI data from schizophrenia and controls, CTE yields results consistent with previous findings but with more significant group differences. The proposed method detects new directed connectivity related to the right frontal parietal regions and achieves 10.2-20.9% higher SVM classification accuracy when inferring directed connectivity using anatomical automatic labeling (AAL) regions as features.

Conclusion

The proposed CTE provides a new general method for fully detecting highly predictive directed connectivity from complex-valued fMRI data, with magnitude-only fMRI data as a specific case.

Different patterns of structural network impairments in two amyotrophic lateral sclerosis subtypes driven by 18F-fluorodeoxyglucose positron emission tomography/magnetic resonance hybrid imaging

The structural network damages in amyotrophic lateral sclerosis patients are evident but contradictory due to the high heterogeneity of the disease. We hypothesized that patterns of structural network impairments would be different in amyotrophic lateral sclerosis subtypes by a data-driven method using 18F-fluorodeoxyglucose positron emission tomography/magnetic resonance hybrid imaging. The data of positron emission tomography, structural MRI and diffusion tensor imaging in fifty patients with amyotrophic lateral sclerosis and 23 healthy controls were collected by a 18F-fluorodeoxyglucose positron emission tomography/magnetic resonance hybrid. Two amyotrophic lateral sclerosis subtypes were identified as the optimal cluster based on grey matter volume and standardized uptake value ratio. Network metrics at the global, local and connection levels were compared to explore the impaired patterns of structural networks in the identified subtypes. Compared with healthy controls, the two amyotrophic lateral sclerosis subtypes displayed a pattern of a locally impaired structural network centralized in the sensorimotor network and a pattern of an extensively impaired structural network in the whole brain. When comparing the two amyotrophic lateral sclerosis subgroups by a support vector machine classifier based on the decreases in nodal efficiency of structural network, the individualized network scores were obtained in every amyotrophic lateral sclerosis patient and demonstrated a positive correlation with disease severity. We clustered two amyotrophic lateral sclerosis subtypes by a data-driven method, which encompassed different patterns of structural network impairments. Our results imply that amyotrophic lateral sclerosis may possess the intrinsic damaged pattern of white matter network and thus provide a latent direction for stratification in clinical research.