Brain Informatics

INTRODUCTION: The digital clock drawing test (dCDT) is a cognitive screening tool capturing high-resolution pen-movement data. Existing approaches for classifying cognitive impairment from dCDTs rely on selected features or image-based methods. We propose representing dCDTs as structural networks to classify cognitive impairment and characterize spatial organization in clock drawings. METHODS: Weighted networks were constructed from 3,362 Framingham Heart Study participants, with and without cognitive impairment. Network representations were generated separately for command and copy conditions, with nodes representing pre-defined clock components (digits, hands, clock face) and edges capturing pairwise spatial relationships between components. Random forest models and graph convolutional networks were trained to predict cognitive impairment from the networks. Models were benchmarked against demographics-only and established summary feature baselines. RESULTS: Network-based models achieved AUC values comparable to the established summary feature baseline across command-only and copy-only conditions. Differences in spatial organization between the cognitively intact and impaired groups were greatest in the upper-right clock region, involving digits 1 and 2, the clock hands, and the surrounding clock face. Differences between the command and copy networks were larger among cognitively impaired participants. DISCUSSION: The proposed network representations provide an objective, scalable, and flexible framework for representing clock drawings for use in classification models. dCDT networks matched the predictive performance of established summary feature-based approaches and identified clock regions where spatial organization differed by cognitive status. After external validation, this approach has the potential to be incorporated within a clinical workflow to support automated and interpretable cognitive screening.

Background: Ultra-low field (ULF) magnetic resonance imaging (MRI) offers accessible point-of-care neuroimaging, yet its capability to support multivariate network analyses remains understudied. Methods: We systematically evaluated the feasibility and reliability of Morphometric Similarity Networks (MSN) and Morphometric Inverse Divergence (MIND) networks derived from paired high-field (HF, 3T/1.5T) and ULF (64mT) scans in a primary cohort(N=23) and an independent replication cohort (N=19). We tested the impact of super-resolution processing, cortical parcellation granularity (Desikan-Killiany vs. Destrieux), and mesh resolution (fsnative vs. fsaverage5) on edge-level cross-scanner agreement (Lin's concordance correlation coefficient, CCC) and subject-level identifiability. Results: MIND networks demonstrated higher edge-level HF-ULF agreement (mean CCC = 0.95) and ULF reproducibility (mean CCC = 0.98) compared to MSNs (mean CCC = 0.86-0.87 and 0.91-0.94, respectively). In contrast, MSNs yielded higher subject-level differential identifiability across field strengths due to a broader distinction between within- and between-subject similarity. Reconstructing MIND networks on native high-resolution meshes (fsnative) maximised cross-scanner agreement, whereas coarser meshes and finer cortical parcellations enhanced individual network identifiability. Conclusions: Structural similarity networks can be reliably constructed from ULF MRI data. Optimal analytic choices depend on study objectives: MIND on native meshes maximises cross-scanner stability, whereas MSNs excel at preserving individual uniqueness, paving the way for globally scalable connectomic research.

Longitudinal clinical cohorts contain heterogeneous progression patterns that are obscured by coarse diagnostic labels. Discovering these latent progression phenotypes requires models that distinguish patients not only by their outcomes, but also by how their clinical trajectories evolve. This paper proposes TRACE, an interpretable temporal phenotyping method that enables the learning of outcome-informed progression phenotypes from longitudinal trajectories. TRACE combines a trajectory- and outcome-aware contrastive objective with a probabilistic predictive phenotype layer, encouraging phenotypes to capture progression structure while remaining predictive of diagnostic state. A dual-attention mechanism further supports interpretation by identifying the visits and clinical variables most associated with each phenotype assignment. TRACE is evaluated on the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort for cognitive disease modeling, showing improved performance in both diagnostic prediction and phenotype discovery compared with other temporal phenotyping baselines. Finally, by allowing the number of phenotypes to exceed the diagnostic labels, TRACE enables discovering additional progression patterns that are not captured by labels defined a priori.

Structural connectomics provides a means of characterising white matter (WM) network organisation using diffusion MRI (dMRI), but reconstructing neonatal structural connectomes remains challenging due to the immature and heterogeneous nature of the developing brain. Although recent neonatal studies have increasingly adopted approaches beyond conventional diffusion tensor imaging (DTI), there remains scope to incorporate more advanced tractography and streamline post-processing approaches within neonatal pipelines. Here, we propose a neonatal structural connectome pipeline integrating surface-based anatomical constraints, ensemble tractography, hierarchical anatomical refinement of pathways (HARP) filtering, and spherical-deconvolution informed filtering (SIFT2) reweighting. We acquired 3T MRI data from 300 neonates (185 preterm, 115 term-born) to assess the impact of pipeline configuration on network organisation. Mean gestational age (GA) at birth was 29.31 (23.43-32.86) weeks in preterm infants and 39.52 (37.00-42.43) weeks in term infants. At scan, mean GA was 40.85 (36.29-49.00) weeks in preterm infants and 42.06 (38.29-48.00) weeks in term infants. Four connectome variants were generated: raw surfaced-based tractograms, HARP-filtered, SIFT2-weighted, and combined HARP+SIFT2. Pipeline performance was primarily evaluated using an existing brain age prediction model: connectome-based predictive model (CPM) and our derived connectomes to estimate GA at scan in term and preterm infants. In addition, global graph theoretical metric analyses were conducted to provide a complementary assessment of pipeline configuration differences in network organisation. In total, seven graph theoretical metrics were calculated and evaluated: global efficiency, local efficiency, transitivity, modularity, assortativity, degree variance and statistical complexity. Across all configurations, connectomes supported accurate brain age prediction, with mean absolute errors (MAE) of 0.76-0.79 weeks in term infants and 1.00-1.06 weeks in preterm infants. Prediction error and brain age gap variability were greater in preterm than term infants across configurations, with HARP+SIFT2 achieving the lowest preterm MAE (1.00 weeks) and brain age gap (BAG) variability (SD = 1.38 weeks). However, differences in prediction performance across configurations were minimal, suggesting limited influence of pipeline choice on brain age prediction. However, analysis of global network metrics demonstrated that pipeline choice significantly influenced downstream measures of network organisation, with HARP+SIFT2 being the only configuration to identify significant preterm vs. term differences across all seven metrics examined. These findings demonstrate the importance of considering tractography and streamline-processing choices when constructing neonatal structural connectomes and provide a framework for more anatomically informed investigation of early brain development.

Purpose: To evaluate whether visually assessed high-quality prostate MRI, as defined by the Prostate Imaging Quality (PI-QUAL) scoring system, is sufficient to ensure consistent AI model performance, or whether hidden acquisition-related technical effects introduce variability that cannot be detected through visual inspection. Methods: This retrospective study included 568 bpMRI scans from seven institutions, classified using PI-QUAL v2 into high-quality (scores 2-3) and low-quality (score 1) groups. Quantitative image quality features were extracted using MRQy from T2w and ADC sequences. Three experiments were conducted on high-quality scans: (1) unsupervised clustering to identify hidden technical batch effects; (2) V-Net models (M) for prostate capsule segmentation on T2w using single-cluster and balanced multi-cluster training strategies; and (3) VoxelMorph-based deformable registration (M2) to align ADC maps to T2w images. Performance metrics included DSC, precision, recall, volume ratio, HD95, ASSD, and TRE. Kruskal-Wallis tests assessed cross-cluster variability, Two One-Sided Tests (TOST) evaluated performance equivalence, and bootstrap resampling quantified training efficiency. Results: Clustering identified four clusters among 277 high-quality bpMRI scans. Single-cluster models showed variable performance (DSC=0.41-0.85) with significant batch effects (p<0.001), even with 140 training samples. The balanced multi-cluster model (n=60) eliminated batch effects (p=0.075), achieved equivalent performance (p<0.001), and demonstrated 2.23-fold greater training efficiency despite using 2.3-fold fewer samples. Registration performance also varied significantly across clusters (TRE=0.86-5.47 mm, p<0.001). Conclusion: Acquisition-related batch effects significantly impact AI generalizability in prostate MRI, even among high-quality scans. Quantitative clustering enables detection of technical variations and supports diversity-aware training strategies that achieve equivalent performance with 2.3-fold fewer samples while eliminating batch effects.

Introduction: Cognitive intraindividual variability (IIV), a measure of fluctuation in performance across time or tasks, is an emerging marker of cognitive decline. However, little is known about within-domain IIV in Latino adults. To address this gap, we examined memory IIV and hippocampal integrity in older Latino individuals. Methods: Sixty-seven participants from the Boston Latino Aging Study underwent cognitive testing and MRI. Linear regressions assessed whether memory IIV was associated with hippocampal volume and hippocampus-cingulum tract fractional anisotropy. Additional analyses evaluated associations using mean memory performance as the outcome. Results: Higher mean performance was associated with greater hippocampal volume (=0.24, p=.03), while memory IIV was not (=0.028, p=.84). Higher memory IIV was associated with lower fractional anisotropy in the hippocampus-cingulum tract (=-0.34, p<.01), while mean performance was not (=-0.07, p=.52). Discussion: Memory IIV may be more closely linked to hippocampal white matter, while mean memory performance may better reflect hippocampal volume.

The -Bonacci weighted sequence space {ell}(,) is introduced for [&ge;] 2, 1 [&le;] < {infty}, and > 0). It is proved to be a Banach space which is isometrically isomorphic to {ell}, with an unconditional Schauder basis. The space is also reflexive and uniformly convex. Its strict inclusion chain, continuous dual, and asymptotic truncation estimate of order - / are also established. Based on this framework, Scaled &#119898-BonacciNet is proposed as a lightweight CNN that rescales Fibonacci-Net channels using the dominant &#119898-Bonacci root and employs depth wise separable convolutions. Tribonacci-Net achieved 96.09% accuracy and a 0.9581 macro F1-score on the 15-class Brain MRI dataset using 0.85 million parameters and 0.366 GFLOPs. Pentanacci-Net attained 95.87% accuracy on the 44-class dataset while reducing parameters and FLOPs by 47.10% and 44.48%, respectively. On the CKD ultrasound dataset, Hexanacci-Net achieved 83.75% accuracy and a 0.7941 macro F1-score, with corresponding reductions of 50.37% and 45.10%. These findings support dominant-root scaling as a structured approach to lightweight CNN design.

Background: Cross-cohort machine-learning studies of tau positivity are increasingly used to assess generalizability of Alzheimer's disease biomarker models, but apparent differences between cohorts may reflect clinical severity structure and cohort stage composition rather than genuine biological divergence. Methods: We evaluated this problem across subject-level cohorts from the Alzheimer's Disease Neuroimaging Initiative (ADNI), the Open Access Series of Imaging Studies (OASIS3), and the National Alzheimer's Coordinating Center (NACC) using harmonized tabular predictors and a feature-block ablation framework. For ADNI and OASIS3, we performed matched-feature analyses restricted to shared harmonized variables (age, APOE e4, education, sex, the CDR(R) Dementia Staging Instrument sum-of-boxes score (CDR-SB), and the Functional Activities Questionnaire (FAQ)). Models were trained with five-fold stratified cross-validation using balanced logistic regression as the primary classifier. Random forests and extra-trees classifiers were evaluated as robustness checks and are reported in supplementary analyses. Results: In the primary matched-feature comparison, reference areas under the receiver operating characteristic curve (AUCs) were 0.851 for ADNI tau90, 0.860 for ADNI tau180, and 0.933 for OASIS3 tau180. Severity-only models retained 86-99% of reference performance, whereas demographic/genetic-only models retained 85-87%; bootstrap confidence intervals showed that the reference-minus-demo contrast remained positive across all primary cohorts. Before feature matching, OASIS3 showed substantially weaker demographic-only prediction (AUC approx. 0.54), attributable to missing APOE and age variables. After harmonization with a matched feature set including age, the pre-harmonization demographic gap was no longer attributable to feature availability; the residual difference was most consistent with cohort disease-stage composition asymmetry, with CDR-SB = 0 prevalence of 84.5-88.7% in OASIS3 versus 49.3-51.7% in ADNI. Conclusions: The original large ADNI-OASIS3 performance gap was largely a feature-availability artifact; after strict harmonization including age-field repair, the absolute gap narrowed and the signed ADNI-minus-OASIS3 difference reversed, with the residual difference most consistent with cohort disease-stage composition asymmetry. Clinical severity remains an important predictor across all cohorts, but the unique contributions of severity and demographic/genetic information depend strongly on stage composition. Cross-cohort biomarker modeling should account for both feature-set completeness and disease-stage composition heterogeneity, not only headline performance metrics.

Introduction: This study aimed to characterize the neurochemical profile of Alzheimer s disease (AD) continuum stages using proton magnetic resonance spectroscopy (1H-MRS) data for brain metabolites and macromolecules. We used Orthogonal Projection to Latent Structures Discriminant Analysis (OPLS-DA) to classify diagnostic groups and assess feature importance. Methods: Our dataset consisted of 7 T short-echo time 1H-MRS spectra of the posterior cingulate cortex/precuneus of individuals diagnosed with AD (n = 27) or Mild Cognitive Impairment (MCI, n = 27) and Healthy Controls (HC, n = 31). Classification used 100-round Monte Carlo cross-validation based on metabolite concentrations and macromolecular peak intensities. Variable importance in projection (VIP) was assessed across classification models to identify the most relevant neurochemical features. The resulting metabolite rankings were compared with previous literature to evaluate their biological plausibility. Results: Classification was strongest for AD versus HC (AUC=0.88) and lower for comparisons involving MCI. Metabolites contributing most strongly to classification included myo-inositol, ascorbate, and choline-containing compounds, consistent with previous studies on AD-related neurochemical alterations. In addition, specific macromolecular components were identified as relevant features, suggesting a potential contribution of MM signals to the characterization of AD-related neurochemistry. Discussion: These findings support the use of short-echo-time 1H-MRS as a complementary tool for characterizing neurochemical changes in the AD continuum, with potential applications in disease characterization and monitoring, warranting validation in larger cohorts.

Objectives: To determine how strongly clinical referral text, rather than image content, drives the decisions of multimodal large language models (LLMs) differentiating glioma from brain abscess on MRI. Materials and methods: This retrospective single-center study included 60 contrast-enhanced T1-weighted brain MRI examinations (30 gliomas, 30 abscesses; reference-standard diagnosis). One axial slice per case was paired with five referral texts (explicitly inflammatory to explicitly tumor-suggestive) under three prompt instructions and submitted 20 times each to GPT-4o, Claude Sonnet 5, Gemini 3.5 Flash, and Qwen 3.6 (72,000 answers). The primary outcome was the text-driven span (difference in tumor-call frequency between explicit inflammatory and explicit tumor text). Two blinded radiologists classified the images without text; sensitivity, specificity, and accuracy were compared descriptively. Results: Patients (29 women) had a mean age of 55.8 +/- 16.4 years. With neutral text, models answered tumor in 45-67% of queries (specificity, 0.38-0.78), versus 52% and 60% for radiologists (specificity, 0.77 and 0.63). The text-driven span was 0.79-1.00 (p < 0.001 for all models), whereas confirmed gliomas and abscesses differed by only 0.01-0.10 in three models and 0.25 in Gemini 3.5 Flash. Subtle wording produced at least 83% of the span in three models. An instruction to ignore the text reduced the span to 0.48-0.70 in three models and to 0.04 in Gemini 3.5 Flash, partly through reversion to a tumor default. Model accuracy was 0.52-0.73 versus 0.78 and 0.73 for radiologists. Conclusion: Multimodal LLMs deferred to referral text over imaging evidence in differentiating glioma from brain abscess, and prompt instructions did not reliably correct this.

Background: Conventional performance metrics do not indicate whether artificial intelligence (AI)-platforms and radiologists are right or wrong on the same examinations, nor whether their non-agreement is more often beneficial or harmful. Objective: To develop, illustrate, and apply directional metrics quantifying non-agreement between radiologists and AI-platforms against a common reference standard, and to adapt a fragility-based framework for quantifying the statistical robustness of these metrics. Methods: We defined Positive Non-Agreement (PNA, AI-platform correct, radiologist incorrect), Negative Non-Agreement (NNA, radiologist correct, AI-platform incorrect), and Net Non-Agreement (NENA, their difference). Metrics were illustrated in three hypothetical scenarios of 1,000 examinations each and applied to three previously published AI-radiologist studies on pulmonary embolism and mammography. Bootstrap confidence intervals were calculated, and a reclassification fragility index (FI) quantified the minimum case reclassification needed to remove statistical support for NENA's direction. Results: In illustration, NENA was positive in two scenarios and negative in one. In the three published studies, NENA ranged from -4.18% to +10.74% and decreased consistently with increasing radiologist experience (junior residents, +6.27%; consultants, +1.45%), a gradient driven primarily by declining PNA with modest increase in NNA. The reclassification FI was defined for all three illustrative scenarios (range, 0.10%-1.70%) and for six of the seven comparisons from these studies (range, 0.34%-4.31%), but not for the weakest comparison, whose confidence interval for NENA included zero. Conclusions: PNA, NNA, and NENA provide a directional, case-level complement to conventional performance metrics for AI-radiologist non-agreement. The accompanying reclassification FI offers a statistically grounded, generalizable method for quantifying their robustness.

Lesion network mapping (LNM) has provided a compelling framework in clinical neuroscience, linking focal brain damage at heterogeneous anatomical locations to shared functional networks across behavioural syndromes. Recently, the methodological specificity of LNM has come under discussion following observations that derived network maps tend to converge onto the normative connectome's generic degree distribution. While current efforts largely treat this convergence as a statistical inference challenge addressable via post-hoc spatial null models or individual-level analyses, we identify a foundational upstream measurement artifact operating within individual patients: the lesion "bystander effect". Because pathological lesions follow biological constraints rather than functional brain parcellations, a typical lesion encapsulates both a symptom-driving critical node and surrounding asymptomatic bystander tissue. By averaging connectivity profiles across the entire lesion mask, LNM mechanically dilutes focal clinical signals. Across empirical clinical cohorts, individual-level machine learning classifiers, synthetic within-cohort lesion unions, and whole-brain spherical simulations, we demonstrate that increasing lesion extent deterministically drives network maps toward the connectome degree vector following a mathematical power law. Fulfilling the clinical promise of LNM will therefore require computational strategies that isolate the causal functional core from surrounding bystander tissue prior to network projection.

Stroke is a leading cause of mortality and disability in Uganda, with a prevalence of approximately 2.5% and a 25-30% 30-day mortality rate, exacerbated by limited access to predictive tools and advanced diagnostics. Machine learning (ML) offers potential for early stroke risk stratification, but its application in resource-constrained settings like Uganda requires tailored models and rigorous performance evaluation, as well as assessment of misclassification patterns. This study aimed to develop and internally validate an ML-based predictive model for stroke using clinical and socio-demographic characteristics among patients at Mulago National Referral Hospital (MNRH) and residents of Wakiso District, Uganda, identify key predictors and assess misclassification patterns to enhance targeted prevention. A retrospective case-control study utilized secondary data from 1,016 participants (254 stroke cases, 762 controls) across three prior studies conducted between 2006 and 2015. Eight ML algorithms were evaluated using 5-fold cross-validation, with performance metrics including accuracy, sensitivity, positive predictive value, F1-score, and area under receiver operator characteristics curve (AU-ROC). McNemars test compared model classification performance of the top 2 models. Feature importance identified key predictors, and misclassification patterns were analyzed using classification matrix and line graphs. The Random Forest (RF) model achieved the highest performance (95.18% accuracy, 90.10% F1-score, 97% AU-ROC). McNemars test confirmed RFs superior classification (p=0.01). Age, systolic blood pressure, total cholesterol, and waist-hip ratio were the top drivers of RF stroke predictions. RF misclassified 8.3% of stroke cases and 3.3% of non-stroke cases, due to overlaps in risk profiles (e.g., lower cholesterol, systolic blood pressure and age in false negatives). The RF model demonstrates strong potential for stroke risk prediction in Uganda, leveraging routine data to prioritize high-risk individuals. Misclassification analysis highlights areas for model refinement, supporting its potential for integration into clinical workflows for early intervention and stroke prevention in resource-limited settings. Future external validation across diverse populations is recommended.

Syntax processing impairment is a defining feature of primary progressive aphasia (PPA), particularly in the nonfluent/agrammatic variant. However, current models are derived largely from Indo-European languages, and it remains unclear whether the cognitive and neuroanatomical mechanisms of syntax processing generalize to Chinese, an analytic, pro-drop language that encodes grammatical relationships primarily through word order and function words rather than inflectional morphology. Participants were recruited from the international Chinese Language Assessment in PPA cohort and included 64 individuals with PPA (19 logopenic, 15 nonfluent/agrammatic, 15 semantic, and 15 unclassifiable/mixed) and 60 cognitively healthy controls. Participants completed sentence comprehension and production tasks with systematically manipulated syntactic features. Item-level linguistic determinants were examined using ridge regression, diagnostic performance using receiver operating characteristic analyses, and neuroanatomical correlates using voxel-based morphometry of structural MRI. Performance on sentence comprehension and production was influenced by grammatical and lexical features. Subject-relative clauses, passive constructions, a higher proportion of function words, longer character length, and greater lexical content were associated with poorer performance among participants with PPA. Sentence comprehension was impaired in the logopenic, nonfluent/agrammatic, and semantic variants, even when isolated to the syntactic component of the sentence comprehension task. Similarly, sentence production was impaired across all PPA variants (all P < 0.01 versus controls). No significant differences were observed among PPA variants. Adjusted receiver operating characteristic analyses demonstrated high discrimination between each PPA variant and controls, with areas under the curve ranging from 0.87 to 0.97 for sentence comprehension-syntax and from 0.91 to 0.97 for sentence production. However, pairwise comparisons showed no significant differences in discriminative performance among PPA variants after correction for multiple comparisons. Both sentence comprehension-syntax and sentence production showed associations with semantic and repetition measures in addition to motor speech measures. Voxel-based morphometry showed that higher sentence comprehension-syntax scores were associated with greater grey matter volume in left frontotemporal and cingulate regions, whereas higher sentence production scores were additionally associated with greater gray matter volume in frontostriatal structures, including the caudate, putamen, and thalamus. Syntactic processing impairment in Chinese-speaking individuals with PPA extends beyond the nonfluent/agrammatic variant and is associated with distributed frontotemporal and frontostriatal regions, as well as lexical-semantic and auditory verbal short term memory mechanisms. These findings suggest that the cognitive and neural manifestations of syntactic impairment may be shaped by language-specific typological properties, underscoring the need for linguistically informed approaches to the assessment and interpretation of PPA.

Chinese is one of the living languages with the highest homophone densities, making homophone processing a critical component of language comprehension and orthographic decoding, as well as an ideal model for examining its cognitive mechanisms. This study assessed mono- and bi-character homophone selection function in 129 Mandarin- and Cantonese-speaking participants, including 66 cognitively healthy individuals, 17 with semantic variant (sv) primary progressive aphasia (PPA), 23 with the nonfluent/agrammatic variant (nfv) PPA, and 23 with the logopenic variant (lv) PPA. All PPA variants showed impairment in homophone selection, with most significant deficits in svPPA and lvPPA. ROC analyses demonstrated its strong diagnostic utility for distinguishing PPA from controls. Voxel-based morphometry revealed that homophone performance correlated with gray-matter volume in left anterior and inferior temporal regions, with variant-specific patterns observed. These findings described the potential role of homophone selection in diagnosing PPA syndrome in Chinese speakers, illuminate crosslinguistic influences on language impairment in PPA, and provide novel insights into the cognitive processing of homophones.

Awareness of comorbidities in people with dementia is vital context for dementia research and clinical management. Natural Language Processing (NLP) applied to free text in the electronic health record (EHR) may help ascertain comorbidity presence. Our aim was to assess the validity of the MedCAT Comorbidity NLP app in a cohort of people with dementia. We used text from the EHR of South London and Maudsley NHS Mental Health Trust via Clinical Records Interactive Search (CRIS), linked to structured data from local primary care (Lambeth DataNet) and hospital admission data in a cohort of 3,543 people with dementia. We evaluated MedCAT Comorbidity, examining internal validity via manual review of text and external validity by cross-validation against linked primary care and hospital data; and comparison of the predictive value of comorbidity measures for mortality. Internal precision was highest for atrial fibrillation (100%) and lowest for diabetes (78%). Cross-validation with linked data varied substantially across conditions, with precision from 96% to 59% and recall 77% to 4%. The median number of comorbidities identified was two using any single source, or three when combined. MedCAT Comorbidity demonstrated predictive ability for mortality (HR 1.11 per condition), although the model fit was better using comorbidity from hospital admission data. In people with dementia, the MedCAT Comorbidity app showed high precision for most conditions during internal validation. The variability of precision and recall on external validity assessment partly reflects incomplete documentation. Results from the MedCAT comorbidity app can assist researchers in understanding comorbidity of cohorts, particularly when primary care or hospital data is not available. Our findings highlight the need to develop quality assurance protocols that include internal and external validity checks before deploying general data extraction apps for specific clinical subpopulations.

Computed tomography (CT) interpretation requires recognition of the anatomical level of each slice. Classification may facilitate preprocessing and AI-assisted fracture diagnosis. We developed deep learning models to classify craniofacial CT slices into six anatomical categories. A total of 11,769 images were collected from a multicenter database involving 10 Japanese university hospitals and the public Head-Neck-Radiomics-HN1 dataset. Patients were divided into training (70%) and evaluation (30%) sets. Five CNN architectures were trained from scratch, and hard- and soft-voting ensembles were evaluated. Individual-model accuracy ranged from 94.72% to 96.09%; the hard-voting ensemble achieved 96.46% accuracy (Cohen's k = 0.954). Although performance differed among individual models (Cochran's Q = 99.29, p < 0.001), the ensemble did not significantly outperform EfficientNetV2B1 (McNemar's p = 0.163). Errors occurred mainly between adjacent anatomical regions. Deep learning can classify craniofacial CT slices accurately and may support automated dataset organization and AI-assisted fracture diagnosis. External validation is needed.

Neurologic autoantibody cell-based assay images are difficult to interpret when fluorescence is weak, focal or dominated by background signal. We developed a visual artificial intelligence overlay that transfers detected cell boundaries from the structural channel to the antibody-staining image and evaluated standalone model concordance and its association with reader classifications. The analysis used 2 763 records for model development and 499 held-out records spanning 14 antibody targets. Four readers assessed the held-out records first without assistance and then with the boundary overlay and model category available. The selected model agreed with the reader-derived three-class reference in 85.2% of records, but recall was lowest for weak-positive or equivocal records. Reader agreement increased in the assisted session, and almost all revised ratings moved towards the model category. The combined display may support consistent visual review while also creating a risk of model-directed anchoring; its role as a second-read aid requires independent validation.

Patients with implantable cardioverter-defibrillators (ICDs) often are excluded from receiving CMR scans at 3.0T due to ICD-induced image artifacts. This work aims to transfer sequence protocols established for CMR imaging in the presence of ICDs at 1.5T to a first human application at 3.0T. All measurements were performed on a clinical 3T MRI scanner. Phantom measurements were used to establish and optimize wideband PSIR LGE imaging at 3.0T. Volunteer measurements with externally placed ICDs were used for optimization of CINE imaging and wideband PSIR imaging. Thirdly, patients with implanted ICD were examined with the optimized protocols. Human measurements showed that SA stacks obtained with spoiled GRE after contrast agent showed a markedly lower artifact burden than the conventional approach. For scar imaging, a wideband PSIR with a bandwidth of 3kHz and a frequency offset of +750Hz allowed a sufficient suppression of relevant artifacts in the majority of scans. No strong correlation between the image quality and the distance between the cardiac silhouette and the device was detected. The presented work shows the feasibility of clinical cardiac MRI at 3.0T including GRE-CINE and wideband PSIR-LGE for patients with ICDs.

Background and Objectives Transient ischemic attack (TIA) diagnosis is challenging because it relies mainly on clinical assessment and exclusion of common differential. Functional MRI (fMRI) may provide a diagnostic biomarker by detecting transient brain dysfunction not visible on conventional imaging. This pilot study aimed to characterize acute motor-task BOLD activation in patients with suspected DWI-negative TIA and compare functional patterns between confirmed TIA and alternative diagnoses. Secondary objectives were to assess longitudinal changes in activation and connectivity at 3 months. Methods In this prospective single-center study, adults presenting within 24 h of transient anterior-circulation neurological symptoms underwent conventional MRI and additional motor-task fMRI. Patients with acute DWI lesions were excluded. Final diagnosis was established after 3-month follow-up. Whole-brain analyses, individualized motor cortex region-of-interest (ROI) analyses, and generalized psychophysiological interaction (gPPI) connectivity analyses were performed during the acute phase and at 3 months. Results Eight patients were enrolled; six had complete longitudinal imaging data and were included in the final analyses. Final diagnoses were TIA (n=2), migraine aura (n=2), functional neurological disorder (n=1), and hypoglycemia (n=1). Whole-brain and ROI analyses showed reduced bilateral motor-related BOLD responses during the acute phase in both TIA patients and in the patient with hypoglycemia. In contrast, patients with migraine and functional neurological disorder exhibited less marked longitudinal changes. In TIA patients, motor cortex activation increased at 3 months, tending to normalize toward values observed in non-TIA participants. Connectivity analyses revealed heterogeneous patterns, but the 2 TIA patients demonstrated increased longitudinal connectivity between the motor cortex contralateral to symptoms and other sensorimotor regions, including supplementary motor, premotor, parietal, and postcentral cortices. Discussion These preliminary findings suggest that TIA may be associated with transient bilateral reductions in motor-network activity. Functional abnormalities appeared to improve over time, supporting the hypothesis of reversible network dysfunction. Although limited by the very small sample size, this study provides the first longitudinal assessment of emergency task-based fMRI in rigorously defined TIA and supports further investigation of acute functional MRI as a potential biomarker of transient cerebral dysfunction.