Novel Alzheimer's disease subtypes based on functional brain connectivity in human connectome project.

The pathogenesis of Alzheimer's disease (AD) remains unclear, but revealing individual differences in functional connectivity (FC) may provide insights and improve diagnostic precision. A hierarchical clustering-based autoencoder with functional connectivity was proposed to categorize 82 AD patients from the Alzheimer's Disease Neuroimaging Initiative. Compared to directly performing clustering, using an autoencoder to reduce the dimensionality of the matrix can effectively eliminate noise and redundant information in the data, extract key features, and optimize clustering performance. Subsequently, subtype differences in clinical and graph theoretical metrics were assessed. Results indicate a significant inter-subject heterogeneity in the degree of FC disruption among AD patients. We have identified two neurophysiological subtypes: subtype I exhibits widespread functional impairment across the entire brain, while subtype II shows mild impairment in the Limbic System region. What is worth noting is that we also observed significant differences between subtypes in terms of neurocognitive assessment scores associations with network functionality, and graph theory metrics. Our method can accurately identify different functional disruptions in subtypes of AD, facilitating personalized treatment and early diagnosis, ultimately improving patient outcomes.

Multimodal neuroimaging exploration of the mechanisms of sleep quality deterioration after SARS-CoV-2 Omicron infection.

BACKGROUND: To evaluate the neurological alterations induced by Omicron infection, to compare brain changes in chronic insomnia with those in exacerbated chronic insomnia in Omicron patients, and to examine individuals without insomnia alongside those with new-onset insomnia. METHODS: In this study, a total of 135 participants were recruited between January 11 and May 4, 2023, including 26 patients with chronic insomnia without exacerbation, 24 patients with chronic insomnia with exacerbation, 40 patients with no sleep disorder, and 30 patients with new-onset insomnia after infection with Omicron (a total of 120 participants with different sleep statuses after infection), as well as 15 healthy controls who were never infected with Omicron. Neuropsychiatric data, clinical symptoms, and multimodal magnetic resonance imaging data were collected. The gray matter thickness and T1, T2, proton density, and perivascular space values were analyzed. Associations between changes in multimodal magnetic resonance imaging findings and neuropsychiatric data were evaluated with correlation analyses. RESULTS: Compared with healthy controls, gray matter thickness changes were similar in the patients who have and do not have a history of chronic insomnia groups after infection, including an increase in cortical thickness near the parietal lobe and a reduction in cortical thickness in the frontal, occipital, and medial brain regions. Analyses showed a reduced gray matter thickness in patients with chronic insomnia compared with those with an aggravation of chronic insomnia post-Omicron infection, and a reduction was found in the right medial orbitofrontal region (mean [SD], 2.38 [0.17] vs. 2.67 [0.29] mm; P < 0.001). In the subgroups of Omicron patients experiencing sleep deterioration, patients with a history of chronic insomnia whose insomnia symptoms worsened after infection displayed heightened medial orbitofrontal cortical thickness and increased proton density values in various brain regions. Conversely, patients with good sleep quality who experienced a new onset of insomnia after infection exhibited reduced cortical thickness in pericalcarine regions and decreased proton density values. In new-onset insomnia patients post-Omicron infection, the thickness in the right pericalcarine was negatively correlated with the Self-rating Anxiety Scale (r = - 0.538, P = 0.002, PFDR = 0.004) and Self-rating Depression Scale (r = - 0.406, P = 0.026, PFDR = 0.026) scores. CONCLUSIONS: These findings help us understand the pathophysiological mechanisms involved when Omicron invades the nervous system and induces various forms of insomnia after infection. In the future, we will continue to pay attention to the dynamic changes in the brain related to insomnia caused by Omicron infection.

The longitudinal trajectory of CSF sTREM2: the alzheimer's disease neuroimaging initiative.

BACKGROUND: The soluble triggering receptor expressed on myeloid cells 2 (sTREM2) in cerebrospinal fluid (CSF) is considered a biomarker of microglia activity. The objective of this study was to investigate the trajectory of CSF sTREM2 levels over time and examine its association with sex. METHODS: A total of 1,017 participants from the Alzheimer's Disease Neuroimaging Initiative Study (ADNI) with at least one CSF sTREM2 record were included. The trajectory of CSF sTREM2 was analyzed using a growth curve model. The association between CSF sTREM2 levels and sex was assessed using linear mixed-effect models. RESULTS: CSF sTREM2 levels were increased with age over time (P < 0.0001). No significant sex difference was observed in sTREM2 levels across the entire sample; however, among the APOE ε4 allele carriers, women exhibited significantly higher sTREM2 levels than men (ß = 0.146, P = 0.002). CONCLUSION: Our findings highlight the association between CSF sTREM2 levels and age-related increments, underscoring the potential influence of aging on sTREM2 dynamics. Furthermore, our observations indicate a noteworthy association between sex and CSF sTREM2 levels, particularly in individuals carrying the APOE ε4 allele.

Machine learning of dissection photographs and surface scanning for quantitative 3D neuropathology.

We present open-source tools for three-dimensional (3D) analysis of photographs of dissected slices of human brains, which are routinely acquired in brain banks but seldom used for quantitative analysis. Our tools can: (1) 3D reconstruct a volume from the photographs and, optionally, a surface scan; and (2) produce a high-resolution 3D segmentation into 11 brain regions per hemisphere (22 in total), independently of the slice thickness. Our tools can be used as a substitute for ex vivo magnetic resonance imaging (MRI), which requires access to an MRI scanner, ex vivo scanning expertise, and considerable financial resources. We tested our tools on synthetic and real data from two NIH Alzheimer's Disease Research Centers. The results show that our methodology yields accurate 3D reconstructions, segmentations, and volumetric measurements that are highly correlated to those from MRI. Our method also detects expected differences between post mortem confirmed Alzheimer's disease cases and controls. The tools are available in our widespread neuroimaging suite 'FreeSurfer' (https://surfer.nmr.mgh.harvard.edu/fswiki/PhotoTools).

Every year, thousands of human brains are donated to science. These brains are used to study normal aging, as well as neurological diseases like Alzheimer's or Parkinson's. Donated brains usually go to 'brain banks', institutions where the brains are dissected to extract tissues relevant to different diseases. During this process, it is routine to take photographs of brain slices for archiving purposes. Often, studies of dead brains rely on qualitative observations, such as 'the hippocampus displays some atrophy', rather than concrete 'numerical' measurements. This is because the gold standard to take three-dimensional measurements of the brain is magnetic resonance imaging (MRI), which is an expensive technique that requires high expertise ­ especially with dead brains. The lack of quantitative data means it is not always straightforward to study certain conditions. To bridge this gap, Gazula et al. have developed an openly available software that can build three-dimensional reconstructions of dead brains based on photographs of brain slices. The software can also use machine learning methods to automatically extract different brain regions from the three-dimensional reconstructions and measure their size. These data can be used to take precise quantitative measurements that can be used to better describe how different conditions lead to changes in the brain, such as atrophy (reduced volume of one or more brain regions). The researchers assessed the accuracy of the method in two ways. First, they digitally sliced MRI-scanned brains and used the software to compute the sizes of different structures based on these synthetic data, comparing the results to the known sizes. Second, they used brains for which both MRI data and dissection photographs existed and compared the measurements taken by the software to the measurements obtained with MRI images. Gazula et al. show that, as long as the photographs satisfy some basic conditions, they can provide good estimates of the sizes of many brain structures. The tools developed by Gazula et al. are publicly available as part of FreeSurfer, a widespread neuroimaging software that can be used by any researcher working at a brain bank. This will allow brain banks to obtain accurate measurements of dead brains, allowing them to cheaply perform quantitative studies of brain structures, which could lead to new findings relating to neurodegenerative diseases.

Molecular genetics, neuroimaging outcomes, and structural analyses of novel and recurrent variants of WDR62 gene in two consanguineous Pakistani families with autosomal recessive primary microcephaly.

BACKGROUND: Autosomal recessive primary microcephaly (MCPH) is a rare neurodevelopmental and genetically heterogeneous disorder, characterized by small cranium size (> - 3 SD below mean) and often results in varying degree of intellectual disability. Thirty genes have been identified for the etiology of this disorder due to its clinical and genetic heterogeneity. METHODS AND RESULTS: Here, we report two consanguineous Pakistani families affected with MCPH exhibiting mutation in WDR62 gene. The investigation approach involved Next Generation Sequencing (NGS) gene panel sequencing coupled with linkage analysis followed by validation of identified variants through automated Sanger sequencing and Barcode-Tagged (BT) sequencing. The molecular genetic analysis revealed one novel splice site variant (NM_001083961.2(WDR62):c.1372-1del) in Family A and one known exonic variant NM_001083961.2(WDR62):c.3936dup (p.Val1313Argfs*18) in Family B. Magnetic Resonance Imaging (MRI) scans were also employed to gain insights into the structural architecture of affected individuals. Neurological assessments showed the reduced gyral and sulcal patterns along with normal corpus callosum in affected individuals harboring novel variant. In silico assessments of the identified variants were conducted using different tools to confirm the pathogenicity of these variants. Through In silico analyses, both variants were identified as disease causing and protein modeling of exonic variant indicates subtle conformational alterations in prophesied protein structure. CONCLUSION: This study identifies a novel variant (c.1372-1del) and a recurrent pathogenic variant c.3936dup (p.Val1313Argfs*18) in the WDR62 gene among the Pakistani population, expanding the mutation spectrum for MCPH. These findings emphasize the importance of genetic counseling and awareness to reduce consanguinity and address the burden of this disorder.

Leveraging the adolescent brain cognitive development study to improve behavioral prediction from neuroimaging in smaller replication samples.

Neuroimaging is a popular method to map brain structural and functional patterns to complex human traits. Recently published observations cast doubt upon these prospects, particularly for prediction of cognitive traits from structural and resting state functional magnetic resonance imaging (MRI). We leverage baseline data from thousands of children in the Adolescent Brain Cognitive DevelopmentSM Study to inform the replication sample size required with univariate and multivariate methods across different imaging modalities to detect reproducible brain-behavior associations. We demonstrate that by applying multivariate methods to high-dimensional brain imaging data, we can capture lower dimensional patterns of structural and functional brain architecture that correlate robustly with cognitive phenotypes and are reproducible with only 41 individuals in the replication sample for working memory-related functional MRI, and ~ 100 subjects for structural and resting state MRI. Even with 100 random re-samplings of 100 subjects in discovery, prediction can be adequately powered with 66 subjects in replication for multivariate prediction of cognition with working memory task functional MRI. These results point to an important role for neuroimaging in translational neurodevelopmental research and showcase how findings in large samples can inform reproducible brain-behavior associations in small sample sizes that are at the heart of many research programs and grants.

Postmortem imaging reveals patterns of medial temporal lobe vulnerability to tau pathology in Alzheimer's disease.

Our current understanding of the spread and neurodegenerative effects of tau neurofibrillary tangles (NFTs) within the medial temporal lobe (MTL) during the early stages of Alzheimer's Disease (AD) is limited by the presence of confounding non-AD pathologies and the two-dimensional (2-D) nature of conventional histology studies. Here, we combine ex vivo MRI and serial histological imaging from 25 human MTL specimens to present a detailed, 3-D characterization of quantitative NFT burden measures in the space of a high-resolution, ex vivo atlas with cytoarchitecturally-defined subregion labels, that can be used to inform future in vivo neuroimaging studies. Average maps show a clear anterior to poster gradient in NFT distribution and a precise, spatial pattern with highest levels of NFTs found not just within the transentorhinal region but also the cornu ammonis (CA1) subfield. Additionally, we identify granular MTL regions where measures of neurodegeneration are likely to be linked to NFTs specifically, and thus potentially more sensitive as early AD biomarkers.

Imaging neuroinflammation in individuals with substance use disorders.

Increasing evidence suggests a role of neuroinflammation in substance use disorders (SUDs). This Review presents findings from neuroimaging studies assessing brain markers of inflammation in vivo in individuals with SUDs. Most studies investigated the translocator protein 18 kDa (TSPO) using PET; neuroimmune markers myo-inositol, choline-containing compounds, and N-acetyl aspartate using magnetic resonance spectroscopy; and fractional anisotropy using MRI. Study findings have contributed to a greater understanding of neuroimmune function in the pathophysiology of SUDs, including its temporal dynamics (i.e., acute versus chronic substance use) and new targets for SUD treatment.

Estimating individual trajectories of structural and cognitive decline in mild cognitive impairment for early prediction of progression to dementia of the Alzheimer's type.

Only a third of individuals with mild cognitive impairment (MCI) progress to dementia of the Alzheimer's type (DAT). Identifying biomarkers that distinguish individuals with MCI who will progress to DAT (MCI-Converters) from those who will not (MCI-Non-Converters) remains a key challenge in the field. In our study, we evaluate whether the individual rates of loss of volumes of the Hippocampus and entorhinal cortex (EC) with age in the MCI stage can predict progression to DAT. Using data from 758 MCI patients in the Alzheimer's Disease Neuroimaging Database, we employ Linear Mixed Effects (LME) models to estimate individual trajectories of regional brain volume loss over 12 years on average. Our approach involves three key analyses: (1) mapping age-related volume loss trajectories in MCI-Converters and Non-Converters, (2) using logistic regression to predict progression to DAT based on individual rates of hippocampal and EC volume loss, and (3) examining the relationship between individual estimates of these volumetric changes and cognitive decline across different cognitive functions-episodic memory, visuospatial processing, and executive function. We find that the loss of Hippocampal volume is significantly more rapid in MCI-Converters than Non-Converters, but find no such difference in EC volumes. We also find that the rate of hippocampal volume loss in the MCI stage is a significant predictor of conversion to DAT, while the rate of volume loss in the EC and other additional regions is not. Finally, individual estimates of rates of regional volume loss in both the Hippocampus and EC, and other additional regions, correlate strongly with individual rates of cognitive decline. Across all analyses, we find significant individual variation in the initial volumes and the rates of changes in volume with age in individuals with MCI. This study highlights the importance of personalized approaches in predicting AD progression, offering insights for future research and intervention strategies.

Cognitive profile, neuroimaging and fluid biomarkers in post-acute COVID-19 syndrome.

We aimed to characterize the cognitive profile of post-acute COVID-19 syndrome (PACS) patients with cognitive complaints, exploring the influence of biological and psychological factors. Participants with confirmed SARS-CoV-2 infection and cognitive complaints ≥ 8 weeks post-acute phase were included. A comprehensive neuropsychological battery (NPS) and health questionnaires were administered at inclusion and at 1, 3 and 6 months. Blood samples were collected at each visit, MRI scan at baseline and at 6 months, and, optionally, cerebrospinal fluid. Cognitive features were analyzed in relation to clinical, neuroimaging, and biochemical markers at inclusion and follow-up. Forty-nine participants, with a mean time from symptom onset of 10.4 months, showed attention-executive function (69%) and verbal memory (39%) impairment. Apathy (64%), moderate-severe anxiety (57%), and severe fatigue (35%) were prevalent. Visual memory (8%) correlated with total gray matter (GM) and subcortical GM volume. Neuronal damage and inflammation markers were within normal limits. Over time, cognitive test scores, depression, apathy, anxiety scores, MRI indexes, and fluid biomarkers remained stable, although fewer participants (50% vs. 75.5%; p = 0.012) exhibited abnormal cognitive evaluations at follow-up. Altered attention/executive and verbal memory, common in PACS, persisted in most subjects without association with structural abnormalities, elevated cytokines, or neuronal damage markers.

Rationale for issuing neuroimaging requests for patients with primary headaches in China.

OBJECTIVE: To investigate the prevalence of neuroimaging in patients with primary headaches and the clinician-based rationale for requesting neuroimaging in China. DATA SOURCES AND STUDY SETTING: This study included patients with primary headaches admitted to hospitals and clinicians in China. We identified whether neuroimaging was requested and the types of neuroimaging conducted. STUDY DESIGN: This was a cross-sectional study, and convenience sampling was used to recruit patients with primary headaches. Clinicians were interviewed using a combination of personal in-depth and topic-selection group interviews to explore why doctors requested neuroimaging. DATA COLLECTION: We searched for the diagnosis of primary headache in the outpatient and inpatient systems according to the International Classification of Diseases-10 code of patients admitted to six hospitals in three provincial capitals by 2022.We selected three public and three private hospitals with neurology specialties that treated a corresponding number of patients. PRINCIPLE FINDINGS: Among the 2263 patients recruited for this study, 1942 (89.75%) underwent neuroimaging. Of the patients, 1157 (51.13%) underwent magnetic resonance imaging (MRI), 246 (10.87%) underwent both head computed tomography (CT) and MRI, and 628 (27.75%) underwent CT. Fifteen of the 16 interviewed clinicians did not issue a neuroimaging request for patients with primary headaches. Furthermore, we found that doctors issued a neuroimaging request for patients with primary headaches mostly, to exclude the risk of misdiagnosis, reduce uncertainty, avoid medical disputes, meet patients' medical needs, and complete hospital assessment indicators. CONCLUSIONS: For primary headaches, the probability of clinicians requesting neuroimaging was higher in China than in other countries. There is considerable room for improvement in determining appropriate strategies to reduce the use of low-value care for doctors and patients.

Graph convolutional network with attention mechanism improve major depressive depression diagnosis based on plasma biomarkers and neuroimaging data.

BACKGROUND: The absence of clinically-validated biomarkers or objective protocols hinders effective major depressive disorder (MDD) diagnosis. Compared to healthy control (HC), MDD exhibits anomalies in plasma protein levels and neuroimaging presentations. Despite extensive machine learning studies in psychiatric diagnosis, a reliable tool integrating multi-modality data is still lacking. METHODS: In this study, blood samples from 100 MDD and 100 HC were analyzed, along with MRI images from 46 MDD and 49 HC. Here, we devised a novel algorithm, integrating graph neural networks and attention modules, for MDD diagnosis based on inflammatory cytokines, neurotrophic factors, and Orexin A levels in the blood samples. Model performance was assessed via accuracy and F1 value in 3-fold cross-validation, comparing with 9 traditional algorithms. We then applied our algorithm to a dataset containing both the aforementioned protein quantifications and neuroimages, evaluating if integrating neuroimages into the model improves performance. RESULTS: Compared to HC, MDD showed significant alterations in plasma protein levels and gray matter volume revealed by MRI. Our new algorithm exhibited superior performance, achieving an F1 value and accuracy of 0.9436 and 94.08 %, respectively. Integration of neuroimaging data enhanced our novel algorithm's performance, resulting in an improved F1 value and accuracy, reaching 0.9543 and 95.06 %. LIMITATIONS: This single-center study with a small sample size requires future evaluations on a larger test set for improved reliability. CONCLUSIONS: In comparison to traditional machine learning models, our newly developed MDD diagnostic model exhibited superior performance and showed promising potential for inclusion in routine clinical diagnosis for MDD.

[Neuroimaging-based study of scalp acupuncture targets for common psychiatric diseases (Part 2)].

In this study, based on the neuroimaging literature Meta analysis retrieved from Neurosynth platform, the scalp stimulation targets for common psychiatric diseases are developed, which provided the stimulation target protocols of scalp acupuncture for attention deficit hyperactivity disorder, autism spectrum disorder, obsessive-compulsive disorder and schizophrenia. The paper introduces the functions of the brain areas that are involved in each target and closely related to the diseases, and lists the therapeutic methods of common acupuncture/scalp acupuncture and common neuromodulation methods for each disease so as to provide the references for clinical practice. Based on the study results above, the paper further summarizes the overlapped stimulation targets undergoing the intervention with scalp acupuncture for common psychiatric diseases, and the potential relationship between these stimulation targets and treatments with acupuncture and moxibustion.

Neuroimaging for Early Diagnosis of Alzheimer's Disease: a Review.

BACKGROUND: Alzheimer's disease (AD) is a progressive neurodegenerative disease that primarily affects people above the age of 60 all around the world. As of now, the cause is unknown and there is no effective cure. The pathological changes of AD have occurred many years before the onset of the disease, and current treatment techniques can only delay the progression of the disease. Because disease-modifying therapies may be most beneficial in the early stages of AD, the clinical significance of an early diagnosis is emphasized. So far, a variety of imaging technologies and related biomarkers have been used to identify and monitor AD, but there are many imaging technologies; finding the most effective imaging technology can assist medical personnel in interpreting the early stages of AD and can also improve patient treatment opportunities. This is, therefore, the main purpose and back-ground of this study. METHODS: PubMed and other repositories were used in this study to conduct a literature search with various keywords, and relevant articles were reviewed. In this review, different neuroimaging techniques are reviewed which are considered advanced tools to help establish the diagnosis, and in addition, the diagnostic utility, advantages, and limitations of contemporary AD imaging techniques are discussed. RESULTS: The results of the literature review and synthesis show that the prevalence of several in vivo biomarkers helps distinguish affected individuals from healthy controls in the early stages of the disease. Additionally, each current imaging method has its advantages and disadvantages, so no single imaging method is the best diagnostic modality. CONCLUSIONS: This article also reviews and draws conclusions on better ways to use the imaging techniques to improve the likelihood of an early diagnosis of AD. It is suggested that future research could focus on expanding the use of imaging technologies and on identifying novel biomarkers manifesting the earliest stages of AD pathology.

A Systematic Review over the Effect of Early Infant Diet on Neurodevelopment: Insights from Neuroimaging.

The optimization of infant neuronal development through nutrition is an increasingly studied area. While human milk consumption during infancy is thought to give a slight cognitive advantage throughout early childhood in comparison to commercial formula, the biological underpinnings of this process are less well-known and debated in the literature. This systematic review seeks to quantitatively analyze whether early diet affects infant neurodevelopment as measured by various neuroimaging modalities and techniques. Results presented suggest that human milk does have a slight positive impact on the structural development of the infant brain-and that this impact is larger in preterm infants. Other diets with distinct macronutrient compositions were also considered, although these had more conflicting results.

Imaging protocols for non-traumatic spinal cord injury: current state of the art and future directions.

INTRODUCTION: Non-traumatic spinal cord injury (NTSCI) is a term used to describe damage to the spinal cord from sources other than trauma. Neuroimaging techniques such as computerized tomography (CT) and magnetic resonance imaging (MRI) have improved our ability to diagnose and manage NTSCIs. Several practice guidelines utilize MRI in the diagnostic evaluation of traumatic and non-traumatic SCI to direct surgical intervention. AREAS COVERED: The authors review practices surrounding the imaging of various causes of NTSCI as well as recent advances and future directions for the use of novel imaging modalities in this realm. The authors also present discussions around the use of simple radiographs and advanced MRI modalities in clinical settings, and briefly highlight areas of active research that seek to advance our understanding and improve patient care. EXPERT OPINION: Although several obstacles must be overcome, it appears highly likely that novel quantitative imaging features and advancements in artificial intelligence (AI) as well as machine learning (ML) will revolutionize degenerative cervical myelopathy (DCM) care by providing earlier diagnosis, accurate localization, monitoring for deterioration and neurological recovery, outcome prediction, and standardized practice. Some intriguing findings in these areas have been published, including the identification of possible serum and cerebrospinal fluid biomarkers, which are currently in the early phases of translation.

Exploring the potential of representation and transfer learning for anatomical neuroimaging: Application to psychiatry.

The perspective of personalized medicine for brain disorders requires efficient learning models for anatomical neuroimaging-based prediction of clinical conditions. There is now a consensus on the benefit of deep learning (DL) in addressing many medical imaging tasks, such as image segmentation. However, for single-subject prediction problems, recent studies yielded contradictory results when comparing DL with Standard Machine Learning (SML) on top of classical feature extraction. Most existing comparative studies were limited in predicting phenotypes of little clinical interest, such as sex and age, and using a single dataset. Moreover, they conducted a limited analysis of the employed image pre-processing and feature selection strategies. This paper extensively compares DL and SML prediction capacity on five multi-site problems, including three increasingly complex clinical applications in psychiatry namely schizophrenia, bipolar disorder, and Autism Spectrum Disorder (ASD) diagnosis. To compensate for the relative scarcity of neuroimaging data on these clinical datasets, we also evaluate three pre-training strategies for transfer learning from brain imaging of the general healthy population: self-supervised learning, generative modeling and supervised learning with age. Overall, we find similar performance between randomly initialized DL and SML for the three clinical tasks and a similar scaling trend for sex prediction. This was replicated on an external dataset. We also show highly correlated discriminative brain regions between DL and linear ML models in all problems. Nonetheless, we demonstrate that self-supervised pre-training on large-scale healthy population imaging datasets (N≈10k), along with Deep Ensemble, allows DL to learn robust and transferable representations to smaller-scale clinical datasets (N≤1k). It largely outperforms SML on 2 out of 3 clinical tasks both in internal and external test sets. These findings suggest that the improvement of DL over SML in anatomical neuroimaging mainly comes from its capacity to learn meaningful and useful abstract representations of the brain anatomy, and it sheds light on the potential of transfer learning for personalized medicine in psychiatry.

Automated segmentation of epilepsy surgical resection cavities: Comparison of four methods to manual segmentation.

Accurate resection cavity segmentation on MRI is important for neuroimaging research involving epilepsy surgical outcomes. Manual segmentation, the gold standard, is highly labour intensive. Automated pipelines are an efficient potential solution; however, most have been developed for use following temporal epilepsy surgery. Our aim was to compare the accuracy of four automated segmentation pipelines following surgical resection in a mixed cohort of subjects following temporal or extra temporal epilepsy surgery. We identified 4 open-source automated segmentation pipelines. Epic-CHOP and ResectVol utilise SPM-12 within MATLAB, while Resseg and Deep Resection utilise 3D U-net convolutional neural networks. We manually segmented the resection cavity of 50 consecutive subjects who underwent epilepsy surgery (30 temporal, 20 extratemporal). We calculated Dice similarity coefficient (DSC) for each algorithm compared to the manual segmentation. No algorithm identified all resection cavities. ResectVol (n = 44, 88 %) and Epic-CHOP (n = 42, 84 %) were able to detect more resection cavities than Resseg (n = 22, 44 %, P < 0.001) and Deep Resection (n = 23, 46 %, P < 0.001). The SPM-based pipelines (Epic-CHOP and ResectVol) performed better than the deep learning-based pipelines in the overall and extratemporal surgery cohorts. In the temporal cohort, the SPM-based pipelines had higher detection rates, however there was no difference in the accuracy between methods. These pipelines could be applied to machine learning studies of outcome prediction to improve efficiency in pre-processing data, however human quality control is still required.

Applications of Functional Near-Infrared Spectroscopy (fNIRS) Neuroimaging During Rehabilitation Following Stroke: A Review.

Stroke is a cerebrovascular disease that impairs blood supply to localized brain tissue regions due to various causes. This leads to ischemic and hypoxic lesions, necrosis of the brain tissue, and a variety of functional disorders. Abnormal cortical activation and functional connectivity occur in the brain after a stroke, but the activation patterns and functional reorganization are not well understood. Rehabilitation interventions can enhance functional recovery in stroke patients. However, clinicians require objective measures to support their practice, as outcome measures for functional recovery are based on scale scores. Furthermore, the most effective rehabilitation measures for treating patients are yet to be investigated. Functional near-infrared spectroscopy (fNIRS) is a non-invasive neuroimaging method that detects changes in cerebral hemodynamics during task performance. It is widely used in neurological research and clinical practice due to its safety, portability, high motion tolerance, and low cost. This paper briefly introduces the imaging principle and the advantages and disadvantages of fNIRS to summarize the application of fNIRS in post-stroke rehabilitation.

A single-arm, open-label pilot study of neuroimaging, behavioral, and peripheral inflammatory correlates of mindfulness-based stress reduction in multiple sclerosis.

Multiple sclerosis (MS) is a chronic neurological disease frequently associated with significant fatigue, anxiety, depression, and stress. These symptoms are difficult to treat, and prominently contribute to the decreases in quality of life observed with MS. The underlying mechanisms of these "silent" symptoms are not well understood and include not just the psychological responses to a chronic disease, but also biological contributions from bidirectional psycho-neuro-immune (dys)regulation of systemic inflammatory biology. To address these issues, we conducted a prospective, observational pilot study to investigate the psychological, biological, and neuroarchitecture changes associated with a mindfulness-based stress reduction (MBSR) program in MS. The overarching hypothesis was that MBSR modulates systemic and central nervous system inflammation via top-down neurocognitive control over forebrain limbic areas responsible for the neurobiological stress response. 23 patients were enrolled in MBSR and assessed pre/post-program with structural 3 T MRI, behavioral measures, hair cortisol, and blood measures of peripheral inflammation, as indexed by the Conserved Transcriptional Response to Adversity (CTRA) profile. MBSR was associated with improvements across a variety of behavioral outcomes, as well as on-study enlargement of the head of the right hippocampus. The CTRA analyses revealed that greater inflammatory gene expression was related to worse patient-reported anxiety, depression, stress, and loneliness, in addition to lower eudaimonic well-being. Hair cortisol did not significantly change from pre- to post-MBSR. These results support the use of MBSR in MS and elucidate inflammatory mechanisms related to key patient-reported outcomes in this population.