Brain Informatics

ObjectiveThis study investigates the potential advantages of hyperbolic convolutional neural networks (HCNNs) over traditional convolutional neural networks (CNNs) in neuroimaging tasks. Materials and MethodsWe conducted a comparative analysis of HCNNs and CNNs across various medical imaging modalities and diseases, with a focus on a compiled multi-modality neuroimaging dataset. The models were assessed for performance parity, robustness to adversarial attacks, semantic organization of embedding spaces, and generalizability. Zero-shot evaluations were also performed with ischemic stroke non-contrast CT images. ResultsHCNNs matched CNN performance on less complex settings and demonstrated superior semantic organization, and robustness to adversarial attacks. While HCNNs equaled CNNs in out-of-sample datasets identifying Alzheimers disease, in zero-shot evaluations, HCNNs outperformed CNNs and radiologists. DiscussionHCNNs deliver enhanced robustness and organization in the neuroimaging data. This likely underlies why while HCNNs perform similarly to CNNs with respect to in-sample tasks, they confer improved generalizability. Nevertheless, HCNNs encounter efficiency and performance challenges with larger, complex datasets. These limitations underline the need for further optimization of HCNN architectures. ConclusionHCNNs present promising improvements in generalizability and resilience for medical imaging applications, particularly in neuroimaging. Despite challenges with larger datasets, HCNNs enhance performance under adversarial conditions and offer better semantic organization, suggesting valuable potential in generalizable deep learning models in medical imaging and neuroimaging diagnostics.

Non-negative matrix factorization (NMF) produces a factorization that constrains the elements of both the factor matrices to be non-negative. It has been a popular feature extraction method in many applications including neuroimaging. One limitation of the existing softwares of NMF is that they were written in and dependent on the proprietary software of MATLAB. To address this limitation, we introduced an open-source C++ package for performing NMF. To make NMF more accessible to the scientific research community, we describe a NMF algorithm implemented using the Insight Toolkit ITK and Armadillo, a MATLAB style C++ based math library. Armadillo facilitates the computations in linear algebra by calling functions without any need to implement functions in C++. In addition, This framework supports the read and write interface to images specific to neuroscience. Finally, The package supports NMF with multiplicative update and sophisticated initialization methods. We showed that the package has accuracy matching MATLAB and its speed close to that of MATLAB. We used simple simulated images to test its functionality. Then, we demonstrated how the package can be used to analyze neuroimaging data. Specifically, we used the package to find a data-driven set of structural patterns(factor matrices) that are similar across individuals. We validated this factorization method by associating their weighted loading matrices with body mass indices (BMI) of individuals from the human connectome project.

Given the high prevalence of aging-associated cerebral small vessel disease in the general population, accurate detection of the related white matter hyperintensities (WMH) in large-scale magnetic resonance imaging (MRI) studies is of critical importance. The performance of currently available semi-automated and automated methods for WMH classification is hampered by their inherent dependence on MRI contrast parameters and long computational processing time. We sought to improve the accuracy and computational cost of automated WMH detection by creating a whole-brain deep learning-based framework: WHITE-Net. We use a 3D ResUNet architecture trained on manually segmented WMHs from fluid-attenuated inversion recovery MRI (n=141) and test its accuracy in a large-scale dataset (n=192). We demonstrate a good generalizability across WMH lesion loads, different MRI scanner vendors, field strengths, imaging protocols, and MR contrasts. The comparison to existing WMH segmentation tools shows a similar to superior accuracy performance at significantly lower computational cost. WHITE-Net tool performance makes it well-suited for application to large-scale MRI datasets, enabling the study of the aging brain while offering the advantage of detecting early or subtle WMH changes often missed by other methods.

IntroductionGlobally, 55 million individuals have dementia, with an increasing annual incident of 10 million. Enabling development of new multi-modal models can improve the current diagnostic pathways and potentially contribute to early diagnosis and treatment of dementia. Here, we report how multi-modal resources is achieved within the successful Trusted Research Environment (TRE) providing access to 60+ cohort datasets for dementia research, the Dementias Platform UK (DPUK). ObjectivesWe aimed to identify the challenges of the storage, distribution and analysis of neuroimaging data and how we could implement a comprehensive infrastructure to deal with these. The problems we specifically aimed to address were how to: anonymise scans, store large amounts of data, standardise datasets to a common format, extract metadata, provision the data, and allow for analysis. MethodsWhile, data within majority of existing research platforms are focused on a single aspect, DPUK data provides an enriched view of disease dynamic for dementia cohorts by providing access to linkable brain imaging and genomic data at the individual-level. We document various stages and capacities required for multi-modal neuroimaging analysis for dementia and conclude that achieving research ready assets to enable neuroimaging analysis for dementia from existing resources requires an engineered process to facilitate multiple aspects of curation, provisioning and large scale analysis. ResultsWe developed an ingest pipeline for neuroimaging data to meet the requirements set out in the objectives. This involved standardising all datasets to the Brain Imaging Data Structure, defacing scans and anonymising data, using MinIO for data storage and extracting metadata from header information for data discovery and provisioning. ConclusionThe neuroimaging ingest pipeline developed has allowed for the distribution of imaging datasets within DPUK which has facilitated multi-modal research on anonymised and standardised data. Our pipelines create research-ready datasets in a simplified way, reducing the time and effort of getting these datasets ready for data sharing and making the process easier for the data owners.

PurposeMELD Graph is a state-of-the-art artificial intelligence (AI) model for automated detection of focal cortical dysplasia (FCD), but its performance remains limited, highlighting the need to investigate which aspects of the pipeline affect its accuracy. MethodsA retrospective failure-mode analysis of the MELD Graph pipeline was performed in 242 subjects, with model predictions and FreeSurfer segmentations reviewed to classify errors as segmentation-associated or algorithm-related. FCD imaging features salient to humans were quantified, with statistical associations examined for both MELD Graph detection and focal FreeSurfer segmentation failure. ResultsMELD Graph demonstrated overall performance similar to previously published non-harmonized results, achieving a sensitivity of 69%, specificity of 44%, and positive predictive value (PPV) of 75%. Focal FreeSurfer segmentation failures were associated with 21% of false negative patients, 25% of false positive clusters in patients, and 16% of false positive clusters in controls. Higher conspicuity on T1-weighted images was associated with MELD Graph detection, whereas greater conspicuity on T2-FLAIR images relative to T1 was associated with detection failure. Bottom-of-sulcus dysplasia (BOSD) and presence of transmantle sign were not associated with detection. Non-BOSD lesions, higher human conspicuity measures, and low T1 image quality were positively associated with focal FreeSurfer segmentation failures. ConclusionFreeSurfer segmentation failures are a significant potential source of error in the MELD Graph pipeline. FCD imaging features salient to humans and image quality were also associated with variability in the algorithm performance. Robust cortical segmentation and stronger integration of T2-FLAIR imaging features may be beneficial for automated FCD detection tools.

Clinical machine learning (CML)for brain MRI often assumes that more data guarantees better performance, yet added samples can reduce accuracy when they arise from a different distribution, a phenomenon known as the Data Addition Dilemma. We present a systematic study of this issue in longitudinal TBI MRI, where acute baseline scans (S1) and follow-up scans (S2) differ substantially. Using a 14-subject, 28-scan cohort, we quantify the combined effects of intra-subject session shifts and inter-subject variability on severity classification. We evaluate four training schemes: (1) intra-session upper bound (S1[->]S1), (2) cross-session OOD testing (S1[->]S2), (3) pooled training (S1+S2[->]S1,S2), and (4) LOSO-IPA, which adds one unlabeled S2 scan per patient. With a lightweight logistic-regression model on PCA features, we show that naive pooling can degrade accuracy, pooled training trades baseline performance for modest robustness gains, and LOSOIPA recovers accuracy close to the intra-session limit. We recommend per-subject follow-up anchoring and diagonal CORAL alignment to mitigate session effects. These results clarify when additional data help or hinder CML workflows and provide a minimally invasive strategy for reliable longitudinal TBI severity assessment.

Functional brain networks play an essential role in the diagnosis of brain disorders by enabling the identification of abnormal patterns and connections in brain activities. Previous methods often rely on whole brain functional connectivity approaches to construct these networks using Functional Magnetic Resonance Imaging (fMRI) data. However, these approaches introduce noise and overlook localized disruptions within specific brain subnetworks, leading to potential misdiagnoses. To address this challenging issue, we propose mBrainGT, a modular brain graph transformer model that focuses on modular functional connectivity (mFC) to improve the diagnosis of brain disorders. Compared to existing methods, mBrainGT constructs and analyses functional brain subnetworks individually, reflecting the inherent structure of the brain. It captures both local features within each modular network and their interactions through self-attention and cross-attention mechanisms. It also learns global interactions via adaptive fusion. We validate mBrainGT on three benchmark datasets (ADNI, PPMI, and ABIDE). The results demonstrate that mBrainGT outperforms existing methods in diagnostic accuracy, providing more robust and precise representations of the brain network essential for accurate disease detection. Our study highlights the potential of modular connectivity-based graph learning in the refinement of brain disorder diagnostics, offering a more precise and biologically relevant representation of functional brain networks.

Current medicine cannot confidently predict who will recover from post-stroke impairments. Researchers have sought to bridge this gap by treating the post-stroke prognostic problem as a machine learning problem. Consistent with the observation that these impairments are caused by the brain damage that stroke survivors suffer, information concerning where and how much lesion damage they have suffered conveys useful prognostic information for these models. Much recent research has considered how best to encode this lesion information, to maximise its prognostic value. Here, we consider an encoding that, while not novel, has never before been formally examined in this context: continuous lesion images, which encode continuous evidence for the presence of a lesion, both within and beyond what might otherwise be considered the boundary of a binary lesion image. Current state of the art models employ information derived from binary lesion images. Here, we show that those models are significantly improved (i.e., with smaller Mean Squared Error between predicted and empirical outcome scores) when using continuous lesion images to predict a wide range of cognitive and language outcomes from a very large sample of stroke patients. We use further model comparisons to locate the predictive advantage to the provision of continuous lesion evidence beyond the boundary of binary lesion images. The continuous lesion images thus provide a straightforward way to incorporate details of both lesioned and non-lesioned tissue when predicting outcomes after stroke.

Deep learning models based on convolutional neural networks (CNNs) have been used to classify Alzheimers disease or infer dementia severity from 3D T1-weighted brain MRI scans. Here, we examine the value of adding occlusion sensitivity analysis (OSA) and gradient-weighted class activation mapping (Grad-CAM) to these models to make the results more interpretable. Much research in this area focuses on specific datasets such as the Alzheimers Disease Neuroimaging Initiative (ADNI) or National Alzheimers Coordinating Center (NACC), which assess people of North American, predominantly European ancestry, so we examine how well models trained on these data generalize to a new population dataset from India (NIMHANS cohort). We also evaluate the benefit of using a combined dataset to train the CNN models. Our experiments show feature localization consistent with knowledge of AD from other methods. OSA and Grad-CAM resolve features at different scales to help interpret diagnostic inferences made by CNNs.

Amyotrophic Lateral Sclerosis (ALS) is an incurable deadly motor neuron disease that causes the gradual deterioration of nerve cells in the spinal cord and brain. It impacts voluntary limb control and can result in breathing impairment. ALS diagnosis is often challenging due to its symptoms overlapping with other medical conditions and many tests must be performed to rule out other conditions, as easily identifiable biomarkers are still lacking. In this study, we explore T1-weighted (T1w) brain Magnetic Resonance Imaging (MRI), a non-invasive neuroimaging approach which has shown to be a reliable biomarker in many medical fields. Nonetheless, current literature on ALS diagnosis fails to retrieve evidence on how to identify biomarkers from T1w MRI. In this paper, we leverage Artificial Intelligence (AI) methods to unveil the unexplored potential of T1w brain MRI for distinguishing ALS patients from those who have similar symptoms but different diseases (mimicking). We consider a retrospective single-center dataset of brain T1-weighted MRIs collected from 2010 to 2018 recruited from the Piemonte and Valle dAosta ALS register (PARALS). The collection includes 548 patients with ALS and 106 with mimicking diseases. Our goal is to develop and validate a ML diagnostic model based exclusively on T1w MRI distinguishing the two classes. First, we extract a set of radiomic features and two sets of Deep Learning (DL)-based features from MRI scans. Then, using each representation, we train 8 binary classifiers. The best results were obtained by combining DL-based features with SVM classifier, reaching an F1-score of 0.91, and a Precision of 0.88, a Recall of 0.94, and an AUC of 0.7 considering the ALS group as the positive class in the testing set.

Automated vessel segmentation in brain CT angiography (CTA) remains challenging despite the potential benefit of applications. Expert acquisition of reference vessel segmentations is a laborious task. We propose an unsupervised generative deep learning approach that can be trained for vessel segmentation in brain CTA using a large dataset (n=908) of unlabelled brain CTAs and non-contrast enhanced CTs (NCCTs). Our unsupervised approach uses a conditional generative adversarial network (GAN) for CTA to NCCT translation by generating a contrast map that allows for automatic extraction of vessel segmentations. Furthermore, we propose a 3D Frangi filter-based loss function to enhance tubular structures in the contrast map to improve vessel segmentations. We used a hold-out test set of 9 CTA volumes with manually annotated reference segmentations. We compared our unsupervised approach with a state-of-the-art supervised nnUnet, trained and evaluated with test set using 9-fold nested cross-validation. Evaluation metrics included voxel-wise Dice similarity coefficient (DSC), true positive rate (TPR), and false positive rate (FPR). The DSC was 4% lower for the unsupervised approach (DSC: 0.74) compared to the supervised nnUnet (DSC: 0.78). Both the TPR and FPR were higher for the unsupervised approach (TPR: 0.75, FPR/1000 voxels:2.05) compared to the supervised nnUnet (TPR:0.71, FPR/1000 voxels:0.87). Hence, the quantitative results showed that our unsupervised method approaches a supervised state-of-the-art segmentation network. The results demonstrate that an unsupervised generative deep learning approach for the segmentation of intracranial vessels is feasible without laborious manual segmentations. HighlightsO_LITo train supervised segmentation models laborious manual segmentations are needed C_LIO_LIUnsupervised generative deep learning does not require manual segmentations C_LIO_LIOur unsupervised method combines L1, adversarial, and a novel Frangiloss C_LIO_LIVarying loss function combinations can reduce false positives or false negatives C_LIO_LIOur method approached the performance of a state-of-the-art supervised nnUnet C_LI

We introduce an AI-driven approach for robust 3D brain image registration, addressing challenges posed by diverse hardware scanners and imaging sites. Our model trained using an SSIM-driven loss function, prioritizes structural coherence over voxel-wise intensity matching, making it uniquely robust to inter-scanner and intra-modality variations. This innovative end-to-end framework combines global alignment and non-rigid registration modules, specifically designed to handle structural, intensity, and domain variances in 3D brain imaging data. Our approach outperforms the baseline model in handling these shifts, achieving results that align closely with clinical ground-truth measurements. We demonstrate its efficacy on 3D brain data from healthy individuals and dementia patients, with particular success in quantifying brain atrophy, a key biomarker for Alzheimers disease and other brain disorders. By effectively managing variability in multisite, multi-scanner neuroimaging studies, our approach enhances the precision of atrophy measurements for clinical trials and longitudinal studies. This advancement promises to improve diagnostic and prognostic capabilities for neurodegenerative disorders.

Understanding how structural and functional brain networks interact to support cognitive processes remains a central challenge in systems neuroscience. In this study, we investigate the dynamics of structure-function coupling (SFC) at the modular level across different cognitive tasks using multimodal neuroimaging data, including anatomical, diffusion, functional at rest and functional at different tasks. By constructing high-resolution structural and functional connectivity matrices, we assessed intra-modular (SFC-INT) and inter-modular (SFC-EXT) coupling to examine their roles in task-specific reorganization. Our results reveal that variations in SFC during cognitive tasks are primarily driven by changes in inter-modular coupling, emphasizing network integration over segregation. Specifically, tasks demanding higher cognitive flexibility, such as the gender stroop task, exhibited increased SFC-EXT, indicating enhanced integration between modules. In contrast, tasks focused on memory processing showed a tendency toward segregation, with lower SFC-EXT values. These findings highlight the significance of inter-modular integration as a flexible and dynamic mechanism underlying cognitive task discrimination. Our study advances the understanding of modular brain network dynamics, suggesting that the brains ability to integrate information across modules plays a pivotal role in cognitive flexibility and task performance.

BackgroundDeveloping generalizable neuroimaging models is often hindered by limited labeled data which has led to an increased interest in unsupervised inverse learning. Existing approaches often neglect geometric principles and struggle with diverse pathologies. We propose a symmetry-informed inverse learning foundation model to address these shortcomings for robust and efficient anomaly detection in brain MRI. MethodsOur framework employs a reconstruction-to-embedding pipeline, trained exclusively on healthy brain MRI slices. A 2D U-Net uses a novel, symmetry-aware masking strategy to reconstruct a disorder-free slice. Difference maps are embedded into a 1024-dimensional latent space via a Beta-VAE. Anomaly scoring is performed using Mahalanobis distance. We evaluated generalization by fine-tuning on external lesion datasets, BraTS Africa (SSA), and the ADNI-derived Alzheimers disease cohort (Alz). ResultsOn the source metastasis (Mets) dataset, the framework achieved high performance (AB1+MSE: 99.28% accuracy, 99.79% sensitivity). Generalization to the external lesion dataset (SSA) was robust, with the Symmetry ROC configuration achieving 91.93% accuracy. Transfer to the Alzheimers dataset (Alz) was more challenging, achieving a peak accuracy of 70.54% with a high false-positive rate, suggesting difficulty in separating subtle, diffuse changes. ConclusionThe symmetry-informed inverse learning framework establishes a robust foundation model for neuroimaging, showing strong performance for focal lesions and successful generalization under domain shift. Limitations in diffuse neurodegeneration underscore the necessity for richer representations and multimodal integration to improve future foundation models. Summary StatementA symmetry-informed inverse learning framework trained on normal brain MRI achieved high accuracy for detecting focal lesions and demonstrated strong generalization across external datasets under domain shift. Key Points[bullet] A symmetry-informed disorder-free reconstruction framework trained only on normal brain MRI achieved 99.28% accuracy and 99.79% sensitivity for metastasis detection on the BrainMetShare dataset, demonstrating non-inferior performance compared with all but one strategy while offering improved computational efficiency. [bullet]The model generalized effectively to an external tumor dataset (BraTS SSA), achieving up to 91.93% accuracy using receiver operating characteristic-optimized thresholding with minimal fine-tuning. [bullet]Embedding-based anomaly detection using Mahalanobis distance enabled consistent separation between normal and abnormal slices, supporting robust and interpretable anomaly detection across datasets.

Malignant brain tumors are generally classified to be extremely aggressive and often can be fatal when not met with immediate action. Glioblastoma Multiforme is the most common type of malignant tumor found in the brain and is extremely aggressive. For this reason, advanced detection of malignant brain tumors is necessary for optimal mitigation. Conversely, the classification of tumors during Medical Resonance Imaging can be difficult due to bodily movements resulting in the movement of the tumor. The movement of the tumor can disrupt targeted radiotherapy and can also, at times, result in treatments about radiotherapy damaging healthy areas of the brain rather than areas of the tumor. This study proposes a novel deep learning system that can identify tumors from MRI images; which can be helpful for the case of early detection, as well as being able to track tumors during active imaging; resulting in higher efficiency with targeted radiotherapy. This is done utilizing Convolutional Neural Networks (CNNs) created via deep learning frameworks. With the image identification of tumors; 97% accuracy was achieved with optimization. The tumor-classification deep learning system achieved an accuracy of 98%. Further testing is required for optimization; with this optimization, higher accuracy can be reached.

Graph data is an omnipresent way to represent information in machine learning. Especially, in neuroscience research, data from Diffusion-Tensor Imaging (DTI) and functional Magnetic Resonance Imaging (fMRI) is commonly represented as graphs. Exploiting the graph structure of these modalities using graph-specific machine learning applications is currently hampered by the lack of easy-to-use software. PHOTONAI Graph aims to close the gap between domain experts of machine learning, graph experts and neuroscientists. Leveraging the rapid machine learning model development features of the Python machine learning API PHOTONAI, PHOTONAI Graph enables the design, optimization, and evaluation of reliable graph machine learning models for practitioners. As such, it provides easy access to custom graph machine learning pipelines including, hyperparameter optimization and algorithm evaluation ensuring reproducibility and valid performance estimates. Integrating established algorithms such as graph neural networks, graph embeddings and graph kernels, it allows researchers without significant coding experience to build and optimize complex graph machine learning models within a few lines of code. We showcase the versatility of this toolbox by building pipelines for both resting-state fMRI and DTI data in the hope that it will increase the adoption of graph-specific machine learning algorithms in neuroscience research.

Alzheimers disease (AD) causes progressive structural brain changes that precede clinical symptoms by years. Detecting these changes using structural MRI remains challenging, especially in early stages and when relying on visual interpretation alone. Automated semantic segmentation methods offer anatomical precision and objective measurements, but their outputs are rarely used to support human visual assessment. In this study, we explored whether such segmentation outputs can be used to guide a non-expert investigator in developing and applying interpretable diagnostic criteria. We used images from the Alzheimers Disease Neuroimaging Initiative (ADNI) and implemented a structured, segmentation-informed workflow in which a novice with no prior training in radiology or neuroanatomy developed classification rules based on visual appearance and volumetric readouts through three guided pilot phases. In a fourth phase, the investigator applied these criteria to an independent subset of ADNI images while blinded to the respective ADNI participants diagnostic labels. Using an anatomical segmentation model (MAPER) with training data from a pre-release version of the Hammers Adult Brain Atlas Database (120 brain regions), the investigator focused on the piriform cortex (PC). The choice of PC was context-driven, reflecting an ongoing quantitative study of PC volume. A binary classification (AD-like versus CN-like) rule based on PC volume (< or > 430 mm3), supported by assessments of PC shape and global atrophy, yielded an accuracy of 0.71 across 200 cases spanning four diagnostic groups. Accuracy increased to 0.77 when the analysis was restricted to CN and AD cases (with intermediate pathology (MCI) excluded). These results show that segmentation-guided visual workflows can enable non-experts to apply anatomically grounded classification criteria with moderate accuracy. Our framework can be expanded to other regions and promises to be useful for generating interpretable models, for supporting explainable AI, and for accelerating the acquisition of diagnostic skills.

PurposeDeep learning models are increasingly being used in medical diagnostics, but their vulnerability to adversarial perturbations raises concerns about their reliability in clinical applications. Capsule networks (CapsNets) are a promising architecture for medical imaging tasks, given their ability to model spatial relationships and train with smaller amounts of data. Although previous studies have focused on adversarial training approaches to improve robustness, exploring alternative architectures is an underexplored direction for combating poor adversarial stability. Prior work has suggested that CapsNets may exhibit improved robustness to adversarial perturbations compared to convolutional neural networks (CNNs), but performance on adversarial images has not been studied systematically in clinical environments. We evaluated the robustness of CapsNets compared to CNNs and vision transformers (ViTs) across multiple medical image classification tasks. MethodsWe trained two CNNs (ResNet-18 and ResNet-50), one ViT (MedViT), and two CapsNets (DR-CapsNet and BP-CapsNet) on four distinct medical imaging datasets (PneumoniaMNIST, BreastMNIST, NoduleMNIST3D, and BloodMNIST) and one natural image dataset (MNIST). Models were evaluated on adversarial examples generated by projected gradient descent and fast gradient sign method across a range of perturbation bounds. Interpretability experiments, including latent space and Gradient-weighted Class Activation Mapping (Grad-CAM) analyses, were conducted to better understand model stability on adversarial inputs. ResultsCapsNets demonstrated superior robustness under adversarial perturbations compared to CNNs and ViTs across all medical imaging datasets and the natural image dataset. Latent space and Grad-CAM visualizations revealed that CapsNets maintained more consistent embedding representations and attention maps after adversarial perturbations compared to CNNs and ViTs, suggesting that advantages in CapsNet robustness are supported, at least in part, by more stable feature encodings. Bayes-Pearson routing further improved robustness over standard dynamic routing in CapsNets without compromising baseline performance, suggesting a potential architectural improvement. ConclusionCapsNets exhibit intrinsic advantages in adversarial robustness over CNN- and ViT-based models on medical imaging tasks, suggesting they are a reliable alternative for medical image classification. These findings support the use of CapsNets in clinical applications where model reliability is critical.

Magnetic Resonance Imaging (MRI) provides three-dimensional anatomical and physiological details of the human brain. We describe the Integrated Voxel Analysis Method (IVAM) which, through machine learning, classifies MRI images of brains afflicted with early Alzheimers Disease (AD). This fully automatic method uses an extra trees regressor model in which the feature vector input contains the intensities of voxels, whereby the effect of AD on a single voxel can be predicted. The resulting tree predicts based on the following two steps: a K-nearest neighbor (KNN) algorithm based on Euclidean distance with the feature vector to classify whole images based on their distribution of affected voxels and a voxel-by-voxel classification by the tree of every voxel in the image. An Ising model filter follows voxel-by-voxel tree-classification to remove artifacts and to facilitate clustering of classification results which identify significant voxel clusters affected by AD. We apply this method to T1-weighted MRI images obtained from the Open Access Series of Imaging Studies (OASIS) using images belonging to normal and early AD-afflicted individuals associated with a Client Dementia Rating (CDR) which we use as the target in the supervised learning. Furthermore, statistical analysis using a pre-labeled brain atlas automatically identifies significantly affected brain regions. While achieving 90% AD classification accuracy on 198 images in the OASIS dataset, the method reveals morphological differences caused by the onset of AD.

Alzheimers disease detection faces challenges in capturing hippocampal atrophy across multiple anatomical orientations. This study presents a multi-orientation hippocampus-centered 3D CNN with attention mechanism for automated classification. The architecture processes three parallel 40x128x128x1 volumes from sagittal, axial, and coronal orientations. Each branch employs Conv3D layers with dilated convolutions and attention-based feature fusion. Training on ADNI dataset (1008 subjects: 652 normal, 356 Alzheimers) using focal loss achieves AUC-PR values of 0.982-0.990 across five-fold cross-validation. The hippocampus-centered preprocessing uses MNI152 registration and FIRST segmentation. Results demonstrate superior performance with interpretable attention weights for clinical deployment.