Brain Pathology moves to open access.

Computer-aided diagnosis (CAD) and artificial intelligence (AI) are hot topics in the field of clinical imaging and neuro-imaging. Recently numerous methods were proposed. In this research work, a new 3D magnetic resonance head images (MRI) classifier based on KPCA and SVM is presented. The proposed algorithm, called the support vector machine with kernel principal component analysis (SVM-KPCA), aims to classify an MR brain image as normal or pathological. The system first employed the Discrete Wavelet Transform (DWT) to extract features from the images. After feature vector normalization the kernel principal component analysis (KPCA) is applied to reduce the dimensionality of features. The reduced features were then submitted to a support vector machine (SVM). The strategy of k-fold cross-validation was used to enhance generalization of the proposed algorithm. Seven common brain diseases have been used (Alzheimer's disease, Alzheimer's disease plus visual agnosia, glioma, meningioma, Huntington's disease, sarcoma and Pick's disease) as pathological brains, and MR brain images have been collected from ‘Harvard Medical School’ website and ‘Open Access Series of Imaging Studies (OASIS)’ website, to validate the proposed algorithm. Simulation results were compared with the existing algorithms and it was observed that the proposed work outperforms other algorithms. Working on the same dataset in term of accuracy, sensitivity and specificity.

The link between blood pressure and Alzheimer's disease

We read with great interest the response of our esteemed colleagues, Müller and Contarino1, to our recent letter on neuromodulation.2 In our original report, we described a patient exhibiting severe ECG artifacts in local field potential (LFP) recordings, likely attributable to the implantation of the neurostimulator in the left chest, with the implantation site chosen to accommodate the patient's desire for an unrestricted forehand tennis swing. In contrast, Müller and Contarino present a patient with a right-sided implant who successfully resumed tennis after Deep Brain Stimulation (DBS). Notably, chronic LFP recordings in this patient revealed intermittent increases in LFP power that coincided temporally with periods on the tennis court. Together, these complementary cases highlight two key technical limitations of current adaptive DBS systems, cardiac artifacts and movement-related artifacts. As enthusiastic supporters of both adaptive DBS and tennis, we could not resist continuing this exchange into a friendly “third set.” Here, we would like to introduce and emphasize the concept of error tolerance in adaptive DBS. Adaptive DBS algorithms interact with feedback signals exactly as programmed. However, when those input signals are corrupted by artifacts, the resulting control output may diverge from the intended modulation of pathological brain circuits. From a neuroscientific perspective this is frustrating, but from a clinical perspective, the relevance of such deviations may depend on their nature. While some artifacts can continuously obscure clinically relevant information, thereby compromising the algorithm around the clock. Others can occur only in specific situations, as for example during large-scale movements, hereby introducing episodic adaptive control errors. The ideal scenario with adaptive stimulation perfectly and meaningfully matching the patient's underlying neurophysiology at all times is probably unattainable in reality. Not only because of current technical limitations, but also because no neurophysiological biomarker accounts for 100% of clinical or pharmacological variance. Therefore, the need of an error tolerance is inevitable. Accordingly, we must understand and define appropriate margins for algorithmic error and ensure patients’ safety and comfort during brief periods of unintentional stimulation at the upper or lower bounds of the programmed control range. Returning to tennis: in 2025, Jannik Sinner achieved an exceptional 58–6 win–loss record (a 90.6% match–win rate) despite winning only 56% of total points played. This demonstrates that top tennis players barely win more than half of the points they play, yet still achieve outstanding overall outcomes. Similarly, in adaptive DBS, we do not yet know how consistently the therapy must align with patients’ state for it to be perceived as superior to conventional DBS. Identifying the acceptable margin of error, as well as individual factors influencing tolerance to brief periods of suboptimal stimulation, will be essential for guiding the development and programming of adaptive DBS. (1) Research project: A. Conception, B. Organization, C. Execution; (2) Statistical Analysis: not applicable; (3) Manuscript Preparation: A. Writing of the first draft, B. Review and Critique. M.S.: 1A, 1B, 1C, 3A, 3B G.T.: 1A, 1B, 1C, 3A, 3B We gratefully acknowledge the contributions of our Parkinson's and Movement Disorders Centre team, as well as the stereotactic and functional neurosurgery team in patient care. Open access publishing facilitated by Inselspital Universitatsspital Bern, as part of the Wiley - Inselspital Universitatsspital Bern agreement via the Consortium Of Swiss Academic Libraries. Ethical Compliance Statement: The authors confirm that the approval of an institutional review board was not required for this work, and as no individual patient information is presented, informed consent was not required. We confirm that we have read the Journal's position on issues involved in ethical publication and affirm that this work is consistent with those guidelines. Funding Sources and Conflict of Interest: GT has a research agreement with RuneLab, he also receives financial support from Medtronic, Boston and Spirig, not related to the present work; MS receives financial support from Boston Scientific, Medtronic, Zambon and Bial not related to the present work. Financial Disclosures and Conflicts of Interest: GT receives funding from the Swiss National Science Foundation (project number: PZ00P3_202166) and the Swiss Parkinson Association; MS receives funding from Gottfried und Julia Bangerter-Rhyner-Stiftung. Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

BackgroundAlzheimer's Disease can present with heterogenous neurodegenerative patterns. In order to optimize clinical trials and personalized medicine, the identification and characterization of diverse pathological brain patterns associated with AD have become paramount. Optimal approaches to identify such heterogeneity are unknown. The present study employed two distinct clustering approaches, namely HYDRA and CHIMERA, to delineate the spatial pattern of brain atrophy attributable to AD. Methods were applied to MRI scans from the Open Access Series of Imaging Studies (OASIS‐4) project.MethodsHYDRA uses a convex polytope formed by multiple linear hyperplanes that correspond to various pathological patterns, capturing disease subtypes. CHIMERA assesses the pathological transition by transforming NC distribution to separate transformations matching the disease distribution. While both identify spatial patterns, the distinction lies in HYDRA's discriminative analysis of disease subtypes and CHIMERA's generative nature on disease progression through distribution matching.ResultsBoth approaches identified two patterns, or subtypes (Figure 1) with similar CDR results (Figure 2). All subtypes demonstrate marked atrophy within the medial temporal areas, notably the hippocampus. Disparities are evident when assessing subtypes: Subtype‐2 across both methods shows pronounced variations in regions such as superior frontal, middle temporal, parietal cortex, and precuneus, areas paramount in AD pathology. Conversely, HYDRA's Subtype‐1 highlights subtle differences in temporal cortex relative to its Subtype‐2. CHIMERA's Subtype‐1, while mirroring its Subtype‐2 pattern, is less intensified, suggesting an earlier AD stage. Collectively, these patterns concur with recognized AD neuropathological trajectories, pinpointing regions initially impacted in progression. In addition, an in‐depth exploration of subsequent analysis including a longitudinal data evaluation to observe the progression of these patterns over time is slated for later investigation.ConclusionOur findings demonstrate data‐driven approaches to derive clinically meaningful patterns of neurodegeneration. A parallel evaluation of both approaches accentuates the robustness of clustering techniques, revealing consistent and overlapping insights into the intricate pathological landscapes of AD. This convergence in findings bolsters confidence in the reliability of such analytical tools in AD research.