Neuroimaging Data Research Articles

Detection of Alzheimer Disease in Neuroimages Using Vision Transformers: Systematic Review and Meta-Analysis.

Alzheimer disease (AD) is a progressive condition characterized by cognitive decline and memory loss. Vision transformers (ViTs) are emerging as promising deep learning models in medical imaging, with potential applications in the detection and diagnosis of AD. This review systematically examines recent studies on the application of ViTs in detecting AD, evaluating the diagnostic accuracy and impact of network architecture on model performance. We conducted a systematic search across major medical databases, including China National Knowledge Infrastructure, CENTRAL (Cochrane Central Register of Controlled Trials), ScienceDirect, PubMed, Web of Science, and Scopus, covering publications from January 1, 2020, to March 1, 2024. A manual search was also performed to include relevant gray literature. The included papers used ViT models for AD detection versus healthy controls based on neuroimaging data, and the included studies used magnetic resonance imaging and positron emission tomography. Pooled diagnostic accuracy estimates, including sensitivity, specificity, likelihood ratios, and diagnostic odds ratios, were derived using random-effects models. Subgroup analyses comparing the diagnostic performance of different ViT network architectures were performed. The meta-analysis, encompassing 11 studies with 95% CIs and P values, demonstrated pooled diagnostic accuracy: sensitivity 0.925 (95% CI 0.892-0.959; P<.01), specificity 0.957 (95% CI 0.932-0.981; P<.01), positive likelihood ratio 21.84 (95% CI 12.26-38.91; P<.01), and negative likelihood ratio 0.08 (95% CI 0.05-0.14; P<.01). The area under the curve was notably high at 0.924. The findings highlight the potential of ViTs as effective tools for early and accurate AD diagnosis, offering insights for future neuroimaging-based diagnostic approaches. This systematic review provides valuable evidence for the utility of ViT models in distinguishing patients with AD from healthy controls, thereby contributing to advancements in neuroimaging-based diagnostic methodologies. PROSPERO CRD42024584347; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=584347.

Synergistic effects of plasma S100B and MRI measures of cerebrovascular disease on cognition in older adults.

There is growing interest in studying vascular contributions to cognitive impairment and dementia (VCID) and developing biomarkers to identify at-risk individuals. A combination of biofluid and neuroimaging markers may better profile early stage VCID than individual measures. Here, we tested this possibility focusing on plasma levels of S100 calcium-binding protein B (S100B), which has been linked with blood-brain-barrier (BBB) integrity, and neuroimaging measures assessing BBB function (water exchange rate across the BBB (kw)) and cerebral small vessel disease (white matter hyperintensities (WMHs)). A total of 74 older adults without dementia had plasma samples collected and underwent cognitive assessment. A subsample had neuroimaging data including diffusion prepared pseudo-continuous arterial spin labeling (DP-pCASL) for assessment of BBB kw and T2-weighted fluid-attenuated inversion recovery (FLAIR) for quantification of WMHs. Results indicated that higher plasma S100B levels were associated with poorer episodic memory performance (β = - .031, SE = .008, p < .001). Moreover, significant interactions were observed between plasma S100B levels and parietal lobe BBB kw (interaction β = .095, SE = .042, p = .028) and between plasma S100B levels and deep WMH volume (interaction β = - .025, SE = .009, p = .007) for episodic memory. Individuals with the poorest memory performance showed both high plasma S100B and either low BBB kw in the parietal lobe or increased deep WMH burden. Taken together, our results provide support for the combined use of biofluid and neuroimaging markers in the study of VCID.

Identification of Parkinson’s disease using MRI and genetic data from the PPMI cohort: an improved machine learning fusion approach

ObjectiveThis study aim to leverage advanced machine learning techniques to develop and validate novel MRI imaging features and single nucleotide polymorphism (SNP) gene data fusion methodologies to enhance the early identification and diagnosis of Parkinson’s disease (PD).MethodsWe leveraged a comprehensive dataset from the Parkinson’s Progression Markers Initiative (PPMI), which includes high-resolution neuroimaging data, genetic single-nucleotide polymorphism (SNP) profiles, and detailed clinical information from individuals with early-stage PD and healthy controls. Two multi-modal fusion strategies were used: feature-level fusion, where we employed a hybrid feature selection algorithm combining Fisher discriminant analysis, an ensemble Lasso (EnLasso) method, and partial least squares (PLS) regression to identify and integrate the most informative features from neuroimaging and genetic data; and decision-level fusion, where we developed an adaptive ensemble stacking (AE_Stacking) model to synergistically integrate the predictions from multiple base classifiers trained on individual modalities.ResultsThe AE_Stacking model achieving the highest average balanced accuracy of 95.36% and an area under the receiver operating characteristic curve (AUC) of 0.974, significantly outperforming feature-level fusion and single-modal models (p &amp;lt; 0.05). Furthermore, by analyzing the features selected across multiple iterations of our models, we identified stable brain region features [lh 6r (FD) and rh 46 (GI)] and key genetic markers (rs356181 and rs2736990 SNPs within the SNCA gene region; rs213202 SNP within the VPS52 gene region), highlighting their potential as reliable early diagnostic indicators for the disease.ConclusionThe AE_Stacking model, trained on MRI and genetic data, demonstrates potential in distinguishing individuals with PD. Our findings enhance understanding of the disease and advance us toward the goal of precision medicine for neurodegenerative disorder.

Abstract WP190: Predicting Intracranial Atherosclerosis-related Large Vessel Occlusion Using Deep Learning on Neuroimaging and Clinical Data.

Abstract WP190: Predicting Intracranial Atherosclerosis-related Large Vessel Occlusion Using Deep Learning on Neuroimaging and Clinical Data.

Brain state forecasting for precise brain stimulation: Current approaches and future perspectives.

Transcranial magnetic stimulation (TMS) has the potential to yield insights into cortical functions and improve the treatment of neurological and psychiatric conditions. However, its reliability is hindered by a low reproducibility of results. Among other factors, such low reproducibility is due to structural and functional variability between individual brains. Informing stimulation protocols with individual neuroimaging data could mitigate this issue, ensuring accurate targeting of structural brain areas and functional brain states in a subject-by-subject fashion. However, this process poses a set of theoretical and technical challenges. We focus on the problem of online functional targeting, which requires collecting electroencephalography (EEG) data, extracting brain states, and using them to trigger TMS in real time. This stream of operations introduces hardware and software delays in the real time set-up, such that brain states of interest may vanish before TMS delivery. To compensate for delays, it is necessary to process the EEG signal in real time, forecast it, and instruct TMS devices to target forecasted - rather than measured - brain states. Recently, this approach has been adopted successfully in a number of studies, opening interesting opportunities for personalised brain stimulation treatments. However, little has been done to explore and overcome the limitations of current forecasting methods. After reviewing the state of the art in brain state-dependent stimulation, we will discuss two broad classes of forecasting methods and their suitability for application to EEG time series. Subsequently, we will review the evidence in favour of data-driven forecasting and discuss its potential contributions to TMS methodology and the scientific understanding of brain dynamics, highlighting the transformative potential of big open datasets.

Recognition of autism in subcortical brain volumetric images using autoencoding-based region selection method and Siamese Convolutional Neural Network

BackgroundAutism Spectrum Disorder (ASD) is a neurodevelopmental condition that affects social interactions and behavior. Accurate and early diagnosis of ASD is still challenging even with the improvements in neuroimaging technology and machine learning algorithms. It’s challenging because of the wide range of symptoms, delayed appearance of symptoms, and the subjective nature of diagnosis. In this study, the aim is to enhance ASD recognition by focusing on brain subcortical regions, which are critical for understanding ASD pathology. MethodologyFirst, subcortical structures were extracted from a collection of brain MRI datasets using sophisticated processing steps. Next, a 3D autoencoder was trained on these 3D images to help identify brain regions related to ASD. Two distinct feature selection methods were then applied to the features extracted from the encoder. The highest-ranked features were iteratively selected and increased to reconstruct a specific percentage of the brain that represents the most relevant parts for ASD. Finally, a Siamese Convolutional Neural Network (SCNN) was employed as the classifier model. ResultsThe 3D autoencoder stage helped in identifying and reconstructing the significant subcortical regions related to ASD. Based on the studied dataset, high agreement in regions like the Putamen and Pallidum indicated the critical nature of these structures in distinguishing Autism from controls cases. Subsequently, applying SCNN on these selected subcortical regions yielded promising results. For example, using the classifier on the output regions identified by the Mutual Information (MI) features selection method achieved the highest accuracy of 0.66. ConclusionsThis study shows that using a two-stage model involving autoencoder and SCNN can notably improve the classification of ASD from brain MRI volumetric images. Applying an iterative feature extraction approach allowed to achieve a more accurate identification of ASD-related brain areas. This two-stage approach not only improved classification performance but also enhanced the interpretability of the neuroimaging data.

Federated Bayesian network learning from multi-site data.

Identifying functional connectivity biomarkers of major depressive disorder (MDD) patients is essential to advance the understanding of disorder mechanisms and early intervention. Multi-site data arise naturally which could enhance the statistical power of single-site-based methods. However, the main concern is the inter-site heterogeneity and data sharing barriers between different sites. Our objective is to overcome these barries to learn multiple Bayesian networks (BNs) from rs-fMRI data. We propose a federated joint estimator and the corresponding optimization algorithm, called NOTEARS-PFL. Specifically, we incorporate both shared and site-specific information into NOTEARS-PFL by utilizing the sparse group lasso penalty. Addressing data-sharing constraint, we develop the alternating direction method of multipliers for the optimization of NOTEARS-PFL. This entails processing neuroimaging data locally at each site, followed by the transmission of the learned network structures for central global updates. The effectiveness and accuracy of the NOTEARS-PFL method are validated through its application on both synthetic and real-world multi-site resting-state functional magnetic resonance imaging (rs-fMRI) datasets. This demonstrates its superior efficiency and precision in comparison to alternative approaches. We proposed a toolbox called NOTEARS-PFL to learn the heterogeneous brain functional connectivity in MDD patients using multi-site data efficiently and with the data sharing constraint. The comprehensive experiments on both synthetic data and real-world multi-site rs-fMRI datasets with MDD highlight the excellent efficacy of our proposed method.

Abstract TP209: Improving Neuroimaging Data Processing in Clinical Trials Through Automated Cloud-based Analysis

Abstract TP209: Improving Neuroimaging Data Processing in Clinical Trials Through Automated Cloud-based Analysis

Maternal concentrations of perfluoroalkyl sulfonates and alterations in white matter microstructure in the developing brains of young children.

Maternal exposure to per- and polyfluoroalkyl substances (PFAS) has been linked to child neurodevelopmental difficulties. Neuroimaging research has linked these neurodevelopmental difficulties to white matter microstructure alterations, but the effects of PFAS on children's white matter microstructure remains unclear. We investigated associations between maternal blood concentrations of six common perfluoroalkyl sulfonates and white matter alterations in young children using longitudinal neuroimaging data. This study included 84 maternal-child pairs from a Canadian pregnancy cohort. Maternal second trimester blood concentrations of perfluorohexanesulfonate (PFHxS) and five perfluorooctane sulfonate (PFOS) isomers were quantified. Children underwent magnetic resonance imaging scans between ages two and six (279 scans total). Adjusted linear mixed models investigated associations between each exposure and white matter fractional anisotropy (FA) and mean diffusivity (MD). Higher maternal concentrations of perfluoroalkyl sulfonates were associated with higher MD and lower FA in the body and splenium of the corpus callosum of young children. Multiple sex-specific associations were found. In males, PFHxS was negatively associated with FA in the superior longitudinal fasciculus, while PFOS isomers were positively associated with MD in the inferior longitudinal fasciculus (ILF). In females, PFOS isomers were positively associated with FA in the pyramidal fibers and MD in the fornix, but negatively associated with MD in the ILF. Maternal exposure to perfluoroalkyl sulfonates may alter sex-specific white matter development in young children, potentially contributing to neurodevelopmental difficulties. Larger studies are needed to replicate these findings and examine the neurotoxicity of these chemicals.

Using space-filling curves and fractals to reveal spatial and temporal patterns in neuroimaging data

Objective. Magnetic resonance imaging (MRI), functional MRI (fMRI) and other neuroimaging techniques are routinely used in medical diagnosis, cognitive neuroscience or recently in brain decoding. They produce three- or four-dimensional scans reflecting the geometry of brain tissue or activity, which is highly correlated temporally and spatially. While there exist numerous theoretically guided methods for analyzing correlations in one-dimensional data, they often cannot be readily generalized to the multidimensional geometrically embedded setting.Approach. We present a novel method, Fractal Space-Curve Analysis (FSCA), which combines Space-Filling Curve (SFC) mapping for dimensionality reduction with fractal Detrended Fluctuation Analysis. We conduct extensive feasibility studies on diverse, artificially generated data with known fractal characteristics: the fractional Brownian motion, Cantor sets, and Gaussian processes. We compare the suitability of dimensionality reduction via Hilbert SFC and a data-driven alternative. FSCA is then successfully applied to real-world MRI and fMRI scans.Main results. The method utilizing Hilbert curves is optimized for computational efficiency, proven robust against boundary effects typical in experimental data analysis, and resistant to data sub-sampling. It is able to correctly quantify and discern correlations in both stationary and dynamic two-dimensional images. In MRI Alzheimer's dataset, patients reveal a progression of the disease associated with a systematic decrease of the Hurst exponent. In fMRI recording of breath-holding task, the change in the exponent allows distinguishing different experimental phases.Significance. This study introduces a robust method for fractal characterization of spatial and temporal correlations in many types of multidimensional neuroimaging data. Very few assumptions allow it to be generalized to more dimensions than typical for neuroimaging and utilized in other scientific fields. The method can be particularly useful in analyzing fMRI experiments to compute markers of pathological conditions resulting from neurodegeneration. We also showcase its potential for providing insights into brain dynamics in task-related experiments.

Neuroanatomical Insights: Convergence and Divergence of Tinnitus with Normal or Mild Hearing Loss

Objectives: To explore the neuroanatomical abnormalities in idiopathic tinnitus patients by voxel-based morphometry (VBM) and surface-based morphometry (SBM) techniques. To elucidate the central plasticity in tinnitus patients with normal or mild hearing loss from the neuroanatomical insights. Methods: A total of 74 patients with idiopathic tinnitus (43 with normal hearing and 31 with mild hearing loss) and 98 healthy subjects were enrolled. VBM and SBM were employed to analyze neuroimaging data and identify neuroanatomical differences. Results: Our analysis revealed a reduction in gray matter volume and a distinctive pattern of changes in cortical surface features in patients with idiopathic tinnitus, especially in brain regions closely related to the limbic system, such as the bilateral parahippocampal gyrus, bilateral entorhinal cortex, and insula. Tinnitus patients with mild hearing loss have more extensive gray matter volume reduction, and more complex changes in cortical surface features compared to tinnitus patients with normal hearing. In addition, we also found a significant correlation between the Self-Rating Anxiety Scale (SAS), the Self-Rating Depression Scale (SDS), and Montreal Cognitive Assessment (MoCA) scores of patients with idiopathic tinnitus and cortical characteristic parameters in the above brain regions. Conclusions: There are extensive neuroanatomical alterations in tinnitus patients. Mild hearing loss may aggravate the reduction of gray matter volume and change the surface characteristics of the cortex. Anxiety, depression, and cognitive impairment in patients with idiopathic tinnitus may be related to neuroanatomical alterations in specific brain regions.

Enlargement of the human prefrontal cortex and brain mentalizing network: anatomically homogenous cross-species brain transformation

To achieve a better understanding of the evolution of the large brain in humans, a comparative analysis of species differences in the brains of extant primate species is crucial, as it allows direct comparisons of the brains. We developed a method to achieve anatomically precise region-to-region homologous brain transformations across species using computational neuroanatomy. Utilizing three-dimensional neuroimaging data from humans (Homo sapiens), chimpanzees (Pan troglodytes), and Japanese macaques (Macaca fuscata), along with the anatomical labels of their respective brains, we aimed to create a cross-species average template brain that preserves neuroanatomical correspondence across species. Homologous transformation of the brain from one species to another can be computed using the cross-species average brain. Applying this transformation to human and chimpanzee brains revealed that, compared to chimpanzees, humans had significantly larger and more expanded prefrontal cortex, middle and posterior temporal gyrus, angular gyrus, precuneus, and cortical areas associated with mentalization. This neuroanatomically homologous brain transformation enables the systematic investigation of the similarities and differences in brain anatomy and structure across different species.

Fractional amplitude of low-frequency fluctuations during music-evoked autobiographical memories in neurotypical older adults

IntroductionResearchers have shown that music-evoked autobiographical memories (MEAMs) can stimulate long-term memory mechanisms while requiring little retrieval effort and may therefore be used in promising non-pharmacological interventions to mitigate memory deficits. Despite an increasing number of studies on MEAMs, few researchers have explored how MEAMs are bound in the brain.MethodsIn the current study activation indexed by fractional amplitude of low frequency fluctuations (fALFF) during familiar and unfamiliar MEAM retrieval was compared in a sample of 24 healthy older adults. Additionally, we aimed to investigate the impact of age-related gray matter volume (GMV) reduction in key regions associated with MEAM-related activation. In addition to a T1 structural scan, neuroimaging data were collected while participants listened to familiar music (MEAM retrieval) versus unfamiliar music.ResultsWhen listening to familiar compared to unfamiliar music, greater fALFF activation patterns were observed in the right parahippocampal gyrus, controlling for age and GMV. The current findings for the familiar (MEAM) condition have implications for cognitive aging as persons experiencing age-related memory decline are particularly susceptible to volumetric reduction in the parahippocampal cortex. Post-hoc analyses to explore correlations between brain activity and the content of MEAMs were performed using the text analysis program Linguistic Inquiry and Word Count.DiscussionOur findings suggest that MEAM-related activation of the parahippocampal cortex is evident in normative older adults. However, it is yet to be determined whether such brain states are attainable in older adult populations diagnosed with mild cognitive impairment and/or prodromal Alzheimer’s disease.

Longitudinal sex-at-birth and age analyses of cortical structure in the ABCD Study®.

While the brain continues to develop during adolescence, such development may depend on sex-at-birth. However, the elucidation of such differences may be hindered by analytical decisions (e.g., covariate selection to address brain-size differences) and the typical reporting of cross-sectional data. To further evaluate adolescent cortical development, we analyzed data from the Adolescent Brain Cognitive Development StudySM, whose cohort of 11,000+ youth participants with biannual neuroimaging data collection can facilitate understanding neuroanatomical change during a critical developmental window. Doubly considering individual differences in the context of group-level effects, we analyzed regional changes in cortical thickness, sulcal depth, surface area, and volume between two timepoints (∼2 years apart) in 9- to 12-year-olds assigned male or female sex-at-birth. First, we conducted linear mixed-effects models to gauge how controlling for intracranial volume, whole-brain volume (WBV), or a summary metric (e.g., mean cortical thickness) influenced interpretations of age-dependent cortical change. Next, we evaluated the relative changes in thickness and surface area as a function of sex-at-birth and age. Here, we showed that WBV (thickness, sulcal depth, volume) and total cortical surface area were more optimal covariates; controlling for different covariates would have substantially altered our interpretations of overall and sex-at-birth-specific neuroanatomical development. Further, we provided evidence to suggest that aggregate change in how cortical thickness is changing relative to surface area is generally comparable across those assigned male or female sex-at-birth, with corresponding change happening at slightly older ages in those assigned male sex-at-birth. Overall, these results help elucidate neuroanatomical developmental trajectories in early adolescence.Significance Statement While most of our brain's development happens early in life, much of it still happens in adolescence. Because many factors can alter those developmental trajectories, it is important to evaluate the shape/timing of those trajectories (i.e., what generally constitutes typical brain development). Here, we showed that our understanding of those trajectories can be affected by how we choose to analyze them. First, we showed that the way researchers address differences in brain size affects how we interpret regional variation in brain change over time. Further, we showed that it is important to consider how similar patterns of development may simply be happening at different ages in different groups. These results support a relatively novel way of analyzing adolescent brain development.

Taichi on the brain: an activation likelihood estimated meta-analysis of functional neuroimaging data.

Tai Chi Chuan (TCC) is an exercise regimen renowned for its comprehensive benefits to both physical and mental health. The present research endeavor aims to elucidate the neurocognitive impacts of TCC compared to alternative exercise modalities or therapeutic interventions. A systematic meta-analysis was undertaken, encompassing a rigorous review of diverse datasets, wherein 422 scholarly articles were examined, with a subset of 18 articles meeting the stringent criteria for inclusion in the analytical framework. The study cohort comprised 677 participants, characterized by a mean age of 56.52 ± 14.89 years and an average educational attainment of 11.06 ± 3.32 years. Noteworthy alterations in functional neural activity were identified within the superior frontal gyrus. This comprehensive analysis provides significant insights into the enduring neural modifications and the distinctive contributions of TCC to cognitive health. Nevertheless, it is imperative to acknowledge the potential for bias in smaller functional magnetic resonance imaging studies owing to their inconclusive outcomes. This observation underscores the critical need for collaborative, multicenter research initiatives with expanded sample sizes to enhance the robustness and generalizability of future findings.

The association between recent stressful life events and brain structure: a UK Biobank longitudinal MRI study.

Recent stressful life events (SLEs) are an established risk factor for a range of psychiatric disorders. Animal studies have shown evidence of gray matter (GM) reductions associated with stress, and previous work has found similar associations in humans. However longitudinal studies investigating the association between stress and changes in brain structure are limited. The current study uses longitudinal data from the UK Biobank and comprises 4,543 participants with structural neuroimaging and recent SLE data (mean age=61.5years). We analyzed the association between recent SLEs and changes in brain structure, determined using the longitudinal FreeSurfer pipeline, focusing on total GM volume and five a priori brain regions: the hippocampus, amygdala, anterior cingulate cortex, orbitofrontal cortex, and insula. We also examined if depression and childhood adversity moderated the relationship between SLEs and brain structure. Individuals who had experienced recent SLEs exhibited a slower rate of hippocampal decrease over time compared to individuals who did not report any SLEs. Individuals with depression exhibited smaller GM volumes when exposed to recent SLEs. There was no effect of childhood adversity on the relationship between SLEs and brain structure. Our findings suggest recent SLEs are not directly associated with an accelerated decline in brain volumes in a population sample of older adults, but instead may alter brain structure via affective disorder psychopathology. Further work is needed to investigate the effects of stress in younger populations who may be more vulnerable to stress-induced changes, and may yet pinpoint brain regions linked to stress-related disorders.

Machine learning techniques for predicting neurodevelopmental impairments in premature infants: a systematic review.

Very preterm infants are highly susceptible to Neurodevelopmental Impairments (NDIs), including cognitive, motor, and language deficits. This paper presents a systematic review of the application of Machine Learning (ML) techniques to predict NDIs in premature infants. This review presents a comparative analysis of existing studies from January 2018 to December 2023, highlighting their strengths, limitations, and future research directions. We identified 26 studies that fulfilled the inclusion criteria. In addition, we explore the potential of ML algorithms and discuss commonly used data sources, including clinical and neuroimaging data. Furthermore, the inclusion of omics data as a contemporary approach employed, in other diagnostic contexts is proposed. We identified limitations and emphasized the significance of employing multimodal data models and explored various alternatives to address the limitations identified in the reviewed studies. The insights derived from this review guide researchers and clinicians toward improving early identification and intervention strategies for NDIs in this vulnerable population.

Association between glymphatic system function and cognitive impairment in elderly patients with late-onset epilepsy.

Recent studies have shown that late-onset epilepsy (LOE) is accompanied with cognitive decline and increased risk of dementia, particularly Alzheimer's disease (AD). However, the pathophysiological mechanism underlying the cognitive decline in LOE remains unclear. The aim of current study was to evaluate the relationship between glymphatic system (GS) function and cognitive decline in LOE patients using the diffusion tensor imaging (DTI) analysis along the perivascular space (DTI-ALPS). Medical records and neuro-imaging data were obtained from 21 LOE patients with cognitive decline, 14 LOE patients without cognitive decline, and 20 age- and sex-matched healthy controls (HCs). Plasma biomarkers including Aβ42 and Aβ40 were examined using single-molecule array (Simoa) assays. The DTI-ALPS parameter was calculated and correlated with the clinical characteristics of LOE, including age, seizure frequency, duration of epilepsy, Mini-Mental State Examination (MMSE), and Aβ42/40. Regression models were used to evaluate the influencing factors of DTI-ALPS index. LOE patients exhibited a decreased ALPS index and Aβ42/40 compared with the HCs. Post-hoc analysis indicated that the DTI-ALPS index and Aβ42/40 in LOE patients with cognitive decline was significantly lower in relative to LOE patients without cognitive decline and HCs. Spearman correlations showed a negative correlation between DTI-ALPS index and age, seizure frequency and disease duration while a positive correlations between the DTI-ALPS index and Aβ42/40 and MMSE scores in LOE patients. Linear regression analysis suggested that the DTI-ALPS index was independently related to age, Aβ42/40 and MMSE score after correcting for gender, education, and vascular risk factors. Our findings using DTI-ALPS method found a positive correlation between cognitive decline and GS dysfunction in LOE patients, and may indicate a potential internal link between age-related LOEU and dementia formation. Therefore, the DTI-ALPS index may serve as a potential imaging marker for diagnosing and monitoring the GS function in LOE patients.

Neurobrucellosis: a retrospective cohort of 106 patients

BackgroundNeurobrucellosis, a serious central nervous system infection caused by Brucella species, presents significant challenges due to its diverse clinical manifestations and the risk of long-term complications and poor outcomes. Identifying predictors of adverse outcomes is critical for improving patient management and overall prognosis.ObjectivesThis study aimed to evaluate the long-term morbidity and mortality associated with neurobrucellosis and to identify key predictors of adverse outcomes.MethodsWe performed a retrospective cohort study of 106 neurobrucellosis patients treated at two major referral centers in Mashhad, Iran, from March 21, 2011, to March 20, 2022. We analyzed clinical, neuroimaging, and laboratory data, and estimated survival probabilities using Kaplan–Meier analysis. Long-term morbidity was evaluated using the Glasgow Outcome Scale.ResultsThe median age of the cohort was 30 years (IQR: 21.8–46.3). The median length of hospital stay was 11 days (IQR: 7–19.8), with an in-hospital mortality rate of 4.7% (n = 5). Survival probabilities were 92.2% (SE = 0.027) at 1 month and 90.1% (SE = 0.030) at 6 months. The median follow-up duration was 52 months (IQR: 35–77). At follow-up, 23.5% (n = 20) of patients had an unfavorable outcome based on the Glasgow Outcome Scale. Predictors of mortality included older age, altered level of consciousness, seizures, elevated body temperature on admission, and white matter changes on neuroimaging.ConclusionNeurobrucellosis is associated with significant long-term morbidity and mortality. Key predictors of mortality include older age, altered level of consciousness, seizures, elevated body temperature on admission, and white matter changes. Identifying these predictors can help in targeting therapeutic strategies and improving patient outcomes through early intervention and close monitoring.

Dedifferentiation of caudate functional organization is linked to reduced D1 dopamine receptor availability and poorer memory function in aging

Abstract Age-related alterations in cortico-striatal function have been highlighted as an important determinant of declines in flexible, higher-order, cognition in older age. However, the mechanisms underlying such alterations remain poorly understood. Computational accounts propose age-related dopaminergic decreases to impoverish neural gain control, possibly contributing to reduced specificity of cortico-striatal circuits, that are modulated by dopamine, in older age. Using multi-modal neuroimaging data (fMRI, PET) from a large lifespan cohort (n = 180), we assessed the relationship between dopamine D1-like receptors (D1DRs) and cortico-striatal function during rest and an n-back working memory task. The results revealed gradual age-related decreases in the specificity of functional coupling between the centrolateral caudate and cortical association networks during both rest and working memory, which in turn was associated with poorer short and long-term memory performance with older age. Critically, reduced D1DR availability in the caudate and the prefrontal cortex predicted less differentiated caudate-cortical coupling across the lifespan, in part accounting for the age-related declines observed on this metric. These findings provide novel empirical evidence for a key role of dopamine in maintaining functional specialization of cortico-striatal circuits as individuals age, bridging with computational models of deficient catecholaminergic neuromodulation underpinning age-related dedifferentiation of brain function.