Enhancing Diagnostic Accuracy of Neurological Disorders Through Feature-Driven Multi-Class Classification with Machine Learning

How far is arterial spin labeling MRI from a clinical reality? Insights from arterial spin labeling comparative studies in Alzheimer's disease and other neurological disorders.

BackgroundPatients with type 2 diabetes mellitus (T2DM) exhibit a heightened susceptibility to developing dementia, especially those who already present with mild cognitive impairment (MCI). Nevertheless, the fundamental etiology remains elusive, underscoring the pressing need for an objective and precise diagnostic approach in clinical settings. This study investigates the utilization of machine learning algorithms in conjunction with high-resolution sagittal T1-weighted structural imaging to facilitate automated diagnosis of T2DM patients with MCI, differentiating them from both T2DM patients without MCI and healthy controls (HCs).MethodsThirty patients with T2DM and MCI, thirty T2DM patients without MCI, and thirty matched healthy controls (HCs) were enrolled to identify independent biomarkers and develop a diagnostic model for early cognitive impairment in T2DM. Whole-brain structural features-including cortical surface area, volume, thickness, curvature index, folding index, Gaussian curvature, mean curvature, thickness standard deviation, nuclear volume, hippocampal volume, and white matter volume-were extracted from the images of brains using automated segmentation methods. The minimum redundancy maximum relevance (MRMR) method was employed to filter out irrelevant and redundant features, reducing the dimensionality of the dataset. Subsequently, the least absolute shrinkage and selection operator (LASSO) algorithm was applied for further feature selection, ensuring the retention of only the most diagnostic features. The Random Forest (RF) classifier, a powerful machine learning model within the realm of artificial intelligence, was meticulously trained utilizing a curated feature set that had undergone rigorous refinement. To ensure the robust diagnostic performance and generalizability of the established random forest model, a 5-fold cross-validation was employed, providing a dependable estimation of the model’s effectiveness.ResultsThe FreeSurfer software automatically segmented the cerebral imaging data into up to 70 regions. For model establishment, a comprehensive set of 705 structural features, a series of neuropsychological tests, and standard laboratory tests were utilized. Ultimately, 8 features were selected through two feature selection strategies aimed at refining the optimal features. These included bilateral brainstem volume, left hippocampus volume, left transverse temporal gyrus volume, bilateral posterior corpus callosum volume, left medial orbitofrontal cortex Gaussian curvature, glycosylated hemoglobin, blood sugar levels, and the Digit Span Test (DST) backward score. The Random Forest (RF) model, based on the combined features, exhibited the excellent performance, with mean AUCs of 0.959 (95% CI, 0.940–0.997, mean specificity = 94.2%, mean sensitivity = 88.3%, mean accuracy = 88.3% and mean precision = 88.3%) for the training dataset and mean AUCs of 0.887 (95% CI, 0.746–0.992, mean specificity = 85.0%, mean sensitivity = 70.0%, mean accuracy = 70.0% and mean precision = 69.6%) for the testing dataset, based on 5-fold cross-validation.ConclusionThe RF model, leveraging a combination of features, demonstrates high accuracy in diagnosing T2DM with MCI. The exclusion of T2DM patients with complications may limit generalizability to the broader T2DM population, potentially inflating performance estimates. Among these features, 8 optimal indicators comprising 5 structural features, 1 neuropsychological test feature, and 2 standard laboratory test features emerge as the potential independent biomarkers for detecting early-stage cognitive impairment in T2DM patients. These features hold significant importance in understanding the pathophysiological mechanisms of T2DM-related cognitive impairment. Our fully automated model is capable of swiftly processing MRI data, enabling precise and objective differentiation of T2DM with MCI. This model significantly enhances diagnostic efficiency and holds considerable value in clinical practice, facilitating early diagnosis of T2DM with MCI.

Classification between individuals with mild cognitive impairment (MCI) and healthy controls (HC) based on electroencephalography (EEG) has been considered a challenging task to be addressed for the purpose of its early detection. In this study, we proposed a novel EEG feature, the kernel eigen-relative-power (KERP) feature, for achieving high classification accuracy of MCI versus HC. First, we introduced the relative powers (RPs) between pairs of electrodes across 21 different subbands of 2-Hz width as the features, which have not yet been used in previous MCI-HC classification studies. Next, the Fisher’s class separability criterion was applied to determine the best electrode pairs (five electrodes) as well as the frequency subbands for extracting the most sensitive RP features. The kernel principal component analysis (kernel PCA) algorithm was further performed to extract a few more discriminating nonlinear principal components from the optimal RPs, and these components form a KERP feature vector. Results carried out on 51 participants (24 MCI and 27 HC) show that the newly introduced subband RP feature showed superior classification performance to commonly used spectral power features, including the band power, single-electrode relative power, and also the RP based on the conventional frequency bands. A high leave-one-participant-out cross-validation (LOPO-CV) classification accuracy 86.27% was achieved by the RP feature, using a simple linear discriminant analysis (LDA) classifier. Moreover, with the same classifier, the proposed KERP further improved the accuracy to 88.24%. Finally, cascading the KERP feature to a nonlinear classifier, the support vector machine (SVM), yields a high MCI-HC classification accuracy of 90.20% (sensitivity = 87.50% and specificity = 92.59%). The proposed method demonstrated a high accuracy and a high usability (only five electrodes are required), and therefore, has great potential to further develop an EEG-based computer-aided diagnosis system that can be applied for the early detection of MCI.

Undernutrition among children under the age of five is a major public health concern, especially in developing countries. This study aimed to use machine learning (ML) algorithms to predict undernutrition and identify its associated factors. Secondary data analysis of the 2017 Multiple Indicator Cluster Survey (MICS) was performed using R and Python. The main outcomes of interest were undernutrition (stunting: height-for-age (HAZ) < -2 SD; wasting: weight-for-height (WHZ) < -2 SD; and underweight: weight-for-age (WAZ) < -2 SD). Seven ML algorithms were trained and tested: linear discriminant analysis (LDA), logistic model, support vector machine (SVM), random forest (RF), least absolute shrinkage and selection operator (LASSO), ridge regression, and extreme gradient boosting (XGBoost). The ML models were evaluated using the accuracy, confusion matrix, and area under the curve (AUC) receiver operating characteristics (ROC). In total, 8564 children were included in the final analysis. The average age of the children was 926 days, and the majority were females. The weighted prevalence rates of stunting, wasting, and underweight were 17%, 7%, and 12%, respectively. The accuracies of all the ML models for wasting were (LDA: 84%; Logistic: 95%; SVM: 92%; RF: 94%; LASSO: 96%; Ridge: 84%, XGBoost: 98%), stunting (LDA: 86%; Logistic: 86%; SVM: 98%; RF: 88%; LASSO: 86%; Ridge: 86%, XGBoost: 98%), and for underweight were (LDA: 90%; Logistic: 92%; SVM: 98%; RF: 89%; LASSO: 92%; Ridge: 88%, XGBoost: 98%). The AUC values of the wasting models were (LDA: 99%; Logistic: 100%; SVM: 72%; RF: 94%; LASSO: 99%; Ridge: 59%, XGBoost: 100%), for stunting were (LDA: 89%; Logistic: 90%; SVM: 100%; RF: 92%; LASSO: 90%; Ridge: 89%, XGBoost: 100%), and for underweight were (LDA: 95%; Logistic: 96%; SVM: 100%; RF: 94%; LASSO: 96%; Ridge: 82%, XGBoost: 82%). Age, weight, length/height, sex, region of residence and ethnicity were important predictors of wasting, stunting and underweight. The XGBoost model was the best model for predicting wasting, stunting, and underweight. The findings showed that different ML algorithms could be useful for predicting undernutrition and identifying important predictors for targeted interventions among children under five years in Ghana.

d-glutamate, which is involved in N-methyl-d-aspartate receptor modulation, may be associated with cognitive ageing. This study aimed to use peripheral plasma d-glutamate levels to differentiate patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD) from healthy individuals and to evaluate its prediction ability using machine learning. Overall, 31 healthy controls, 21 patients with MCI and 133 patients with AD were recruited. Serum d-glutamate levels were measured using high-performance liquid chromatography (HPLC). Cognitive deficit severity was assessed using the Clinical Dementia Rating scale and the Mini-Mental Status Examination (MMSE). We employed four machine learning algorithms (support vector machine, logistic regression, random forest and naïve Bayes) to build an optimal predictive model to distinguish patients with MCI or AD from healthy controls. The MCI and AD groups had lower plasma d-glutamate levels (1097.79 ± 283.99 and 785.10 ± 720.06 ng/mL, respectively) compared to healthy controls (1620.08 ± 548.80 ng/mL). The naïve Bayes model and random forest model appeared to be the best models for determining MCI and AD susceptibility, respectively (area under the receiver operating characteristic curve: 0.8207 and 0.7900; sensitivity: 0.8438 and 0.6997; and specificity: 0.8158 and 0.9188, respectively). The total MMSE score was positively correlated with d-glutamate levels (r = 0.368, p < 0.001). Multivariate regression analysis indicated that d-glutamate levels were significantly associated with the total MMSE score (B = 0.003, 95% confidence interval 0.002-0.005, p < 0.001). Peripheral plasma d-glutamate levels were associated with cognitive impairment and may therefore be a suitable peripheral biomarker for detecting MCI and AD. Rapid and cost-effective HPLC for biomarkers and machine learning algorithms may assist physicians in diagnosing MCI and AD in outpatient clinics.

Introduction Recent work has characterized polysomnographic (PSG) changes in Alzheimer’s Dementia (AD) that correlate with disease severity and precede clinical diagnosis, although these findings are limited by relatively small sample sizes and a lack of studies examining sleep microarchitecture. In this study we examined sleep macro- and microarchitectural features in large cohorts of patients with AD and mild cognitive impairment (MCI). Methods Using a database of over 15,000 clinical PSGs with associated clinical metadata, 125 and 173 subjects were identified with mild AD and MCI, respectively. 260 age and sex matched subjects without neurological disease were identified as healthy controls (HC). Sleep staging results were based on AASM criteria, and micro-architectural features were assessed using an algorithm (Wake EEG Similarity Index, WESI) trained to predict state using spectral data from a unique healthy adult cohort. Results Sleep fragmentation was apparent in both the MCI and AD cohorts (relative to healthy controls), with AD subjects demonstrating substantially more severe PSG disturbances. Healthy controls demonstrated 400 minutes of total sleep time, in contrast to 374 minutes for MCI subjects and 362 minutes for AD subjects. Healthy subjects demonstrated 33 minutes of wake after sleep onset (WASO), compared to 60 and 67 minutes for MCI and AD subjects, respectively. As has been previously reported in smaller cohorts, wakeful bouts and N1 durations were substantially prolonged for AD and (to a lesser extent) MCI subjects, while N2, N3, and REM durations were reduced (with a pronounced reduction in AD N3 percentage). Sleep microarchitectural features assessed using WESI demonstrated that AD and MCI subjects had markedly higher relative wakefulness in all sleep stages except for N3, which showed the same sleep depth as HC. Conclusion We confirm increased sleep fragmentation and loss of deeper sleep stages that correlate to disease severity. Spectral features of all sleep stages except N3 appear more wakeful in AD and MCI subjects, suggesting microarousals or impaired sleep maintenance contribute to sleep pathology. While N3 duration was markedly reduced, depth of sleep as measured by the WESI algorithm was comparable between populations. PSG findings offer promise as early diagnostic and therapeutic targets. Support (if any)

Objectives Neuropsychological tests (NPTs) are standard tools for assessing cognitive function. These tools can evaluate the cognitive status of a subject, which can be time-consuming and expensive for interpretation. Therefore, this paper aimed to optimize the systematic NPTs by machine learning and develop new classification models for differentiating healthy controls (HC), mild cognitive impairment, and Alzheimer’s disease dementia (ADD) among groups of subjects. Patients and methods A total dataset of 14,926 subjects was obtained from the formal 46 NPTs based on the Seoul Neuropsychological Screening Battery (SNSB). The statistical values of the dataset included an age of 70.18 ± 7.13 with an education level of 8.18 ± 5.50 and a diagnosis group of three; HC, MCI, and ADD. The dataset was preprocessed and classified in two- and three-way machine-learning classification from scikit-learn (www.scikit-learn.org) to differentiate between HC versus MCI, HC versus ADD, HC versus Cognitive Impairment (CI) (MCI + ADD), and HC versus MCI versus ADD. We compared the performance of seven machine learning algorithms, including Naïve Bayes (NB), random forest (RF), decision tree (DT), k-nearest neighbors (KNN), support vector machine (SVM), AdaBoost, and linear discriminant analysis (LDA). The accuracy, sensitivity, specificity, positive predicted value (PPV), negative predictive value (NPV), area under the curve (AUC), confusion matrixes, and receiver operating characteristic (ROC) were obtained from each model based on the test dataset. Results The trained models based on 29 best-selected NPT features were evaluated, the model with the RF algorithm yielded the best accuracy, sensitivity, specificity, PPV, NPV, and AUC in all four models: HC versus MCI was 98%, 98%, 97%, 98%, 97%, and 99%; HC versus ADD was 98%, 99%, 96%, 97%, 98%, and 99%; HC versus CI was 97%, 99%, 92%, 97%, 97%, and 99% and HC versus MCI versus ADD was 97%, 96%, 98%, 97%, 98%, and 99%, respectively, in predicting of cognitive impairment among subjects. Conclusion According to the results, the RF algorithm was the best classification model for both two- and three-way classification among the seven algorithms trained on an imbalanced NPTs SNSB dataset. The trained models proved useful for diagnosing MCI and ADD in patients with normal NPTs. These models can optimize cognitive evaluation, enhance diagnostic accuracy, and reduce missed diagnoses.

To explore the profile of changes of memory monitoring in patients with mild cognitive impairment (MCI). Rey- auditory verbal learning test (AVLT) and feeling-of-knowing (FOK) test in episodic memory (EM) and semantic memory (SM) were conducted on 30 MCI patients, 17 males and 13 females, aged (66 +/- 9), and 30 age, education level, word fluency test, and digit span-matched healthy persons as healthy control (HC) group. The EM impairment of the MCI group was (13.7 +/- 2.8), significantly higher than that of the HC group [(8.1 +/- 2.0), P < 0.01], but the SM impairment of the MCI group was (5.4 +/- 1.4), not significantly different from that of the HC group [(5.0 +/- 1.4), P > 0.05]. The EM-FOK of the MCI patients with correct judgment and false recognition was (41.8 +/- 13.2)%, significantly higher than that of the HC group [(28.1 +/- 11.6)%, P < 0.01], however, the SM-FOK levels of the MCI patients with different judgment and recognition were all not significantly different from those of the HC group (all P > 0.05). The MCI patients overestimate their memory performance on episodic FOK, but their SM monitoring is not impaired. The deficit of memory monitoring in MCI may be the foundation of subjective memory complaints and is useful for diagnosis of MCI. EM and SM monitoring may depend on different neural mechanisms.

Early separation of mild cognitive impairment (MCI) from normal aging and mild cases of dementia remains a challenge, especially in the general population. We aimed to analyze the diagnostic accuracy of a brief neuropsychological battery (BNB) in dementia and MCI cases from the Neurological Disorders in Central Spain (NEDICES) population-based cohort study. We screened 3,891 participants into dementia and non-dementia groups using a two-phase procedure: screening (MMSE-37 and Pfeffer-11) and clinical diagnosis by specialists (DSM-IV criteria). We selected subsequently a subsample of dementia (n = 98), MCI (n = 71), and cognitively healthy (n = 123) participants matched in socio-demographic characteristics. The clinical validity of each test of the BNB was determined by the area under the ROC curve. We determined the best combination of tests to classify individuals into the diagnostic groups by logistic regression analyses. The results indicated that dementia and MCI groups could be best discriminated from the healthy control group on the basis of their scores on the semantic verbal fluency and delayed recall subtests of the BNB. As for discriminating the MCI group from the dementia group, immediate recall tasks (stories and pictures) yielded the highest level of accuracy. Probably the most interesting finding is that the verbal fluency task consistently allowed discrimination among the diagnostic groups. Overall, subtests of the BNB are more accurate in differentiating dementia patients than MCI patients from healthy controls. In this population-based sample, a more fine-grained discrimination that includes MCI patients should follow a systematic subtest-wise analysis and decision.

An accurate characterization of neural dynamics in mild cognitive impairment (MCI) is of paramount importance to gain further insights into the underlying neural mechanisms in Alzheimer's disease (AD). Nevertheless, there has been relatively little research on brain dynamics in prodromal AD. As a consequence, its neural substrates remain unclear. In the present research, electroencephalographic (EEG) recordings from patients with dementia due to AD, subjects with MCI due to AD and healthy controls (HC) were analyzed using relative power (RP) in conventional EEG frequency bands and a novel parameter useful to explore the spatio-temporal fluctuations of neural dynamics: the spectral flux (SF). Our results suggest that dementia due to AD is associated with a significant slowing of EEG activity and several significant alterations in spectral fluctuations at low (i.e. theta) and high (i.e. beta and gamma) frequency bands compared to HC (p < 0.05). Furthermore, subjects with MCI due to AD exhibited a specific frequency-dependent pattern of spatio-temporal abnormalities, which can help identify neural mechanisms involved in cognitive impairment preceding AD. Classification analyses using linear discriminant analysis with a leave-one-out cross-validation procedure showed that the combination of RP and within-electrode SF at the beta band was useful to obtain a 77.3 % of accuracy to discriminate between HC and AD patients. In the case of comparison between HC and MCI subjects, the classification accuracy reached a value of 79.2 %, combining within-electrode SF at beta and gamma bands. SF has proven to be a useful measure to obtain an original description of brain dynamics at different stages of AD. Consequently, SF may contribute to gain a more comprehensive understanding into neural substrates underlying MCI, as well as to develop potential early AD biomarkers.

Mild cognitive impairment (MCI) is a precursor to Alzheimer's disease. It is imperative to develop a proper treatment for this neurological disease in the aging society. This observational study investigated the effects of acupuncture therapy on MCI patients. Eleven healthy individuals and eleven MCI patients were recruited for this study. Oxy- and deoxy-hemoglobin signals in the prefrontal cortex during working-memory tasks were monitored using functional near-infrared spectroscopy. Before acupuncture treatment, working-memory experiments were conducted for healthy control (HC) and MCI groups (MCI-0), followed by 24 sessions of acupuncture for the MCI group. The acupuncture sessions were initially carried out for 6 weeks (two sessions per week), after which experiments were performed again on the MCI group (MCI-1). This was followed by another set of acupuncture sessions that also lasted for 6 weeks, after which the experiments were repeated on the MCI group (MCI-2). Statistical analyses of the signals and classifications based on activation maps as well as temporal features were performed. The highest classification accuracies obtained using binary connectivity maps were 85.7% HC vs. MCI-0, 69.5% HC vs. MCI-1, and 61.69% HC vs. MCI-2. The classification accuracies using the temporal features mean from 5 seconds to 28 seconds and maximum (i.e, max(5:28 seconds)) values were 60.6% HC vs. MCI-0, 56.9% HC vs. MCI-1, and 56.4% HC vs. MCI-2. The results reveal that there was a change in the temporal characteristics of the hemodynamic response of MCI patients due to acupuncture. This was reflected by a reduction in the classification accuracy after the therapy, indicating that the patients' brain responses improved and became comparable to those of healthy subjects. A similar trend was reflected in the classification using the image feature. These results indicate that acupuncture can be used for the treatment of MCI patients.

Background and objectives: Mild cognitive impairment (MCI) is an intermediate state between normal aging and dementia, and can be divided into amnestic and non-amnestic types. Patients with amnestic MCI present with memory impairments that are often considered as the early manifestation of Alzheimer’s disease. Patients with amnestic MCI are more likely to progress to Alzheimer’s disease than patients with non-amnestic MCI. The U.S. Food and Drug Administration has not yet approved any drug that can treat amnestic MCI. Moxibustion is a common noninvasive traditional oriental intervention, which uses mainly the heat generated by burning herbal preparations containing moxa and mugwort (Artemisia vulgaris) to simulate acupoints for alleviating the symptoms. To date, many clinical studies have investigated the clinical use of moxibustion to improve memory impairments of Alzheimer’s disease, but these have failed to make a distinction between amnestic and non-amnestic MCI. Therefore, this trial has been designed to assess the effectiveness of moxibustion on amnestic MCI using the Montreal Cognitive Assessment Scale. We will also assess the safety of moxibustion in healthy controls, and analyze the variation of brain functional connectivity and effective brain networks in patients with amnestic MCI undergoing moxibustion using function magnetic resonance imaging. Design: This is a prospective, single-center, randomized controlled clinical trial. Methods: This study will enroll 64 patients with amnestic MCI and 48 healthy controls at Baoan People’s Hospital, Shenzhen, China. The first 64 recruited patients with amnestic MCI will be randomly divided into moxibustion, placebo moxibustion, drug, and control groups (n = 16 per group). In the moxibustion group, patients will be given moxa-wool moxibustion for 12 consecutive weeks. The placebo moxibustion group will receive placebo moxibustion on the same acupoints. Patients in the drug group will be given oral administration of donepezil hydrochloride tablets, 5 mg daily, for 12 consecutive weeks. The control group will receive no intervention. Forty-eight healthy controls will also be randomly assigned into moxibustion, placebo moxibustion, and control groups (n = 16 per group). Interventions will be the same as those received by the patients with amnestic MCI. Evaluators will be blind to group allocation. Outcome measures and preliminary results: The primary outcome measure will be the improvement in cognitive function 12 months after treatment. Secondary outcome measures will be the scores on the Montreal Cognitive Assessment Scale, Clinical Dementia Rating Scale, Mini-Mental State Examination Scale, and Activity of Daily Living Scale before treatment, after 12 weeks of treatment, and 6 months after the end of treatment, as well as brain function analysis before treatment and after 12 weeks of treatment and adverse events during treatment and follow-up. A correlation analysis between cognitive function scores and brain function results will be performed. Results of our preliminary study involving 60 patients with amnestic MCI who experienced moxibustion or received no treatment showed that moxibustion on acupoints significantly improved cognitive ability and quality of sleep in patients relative to the baseline and compared with the control group. Moreover, the scores on attention and delayed recall in the moxibustion group after treatment were significantly higher than those at base line. In the control group the scores on visual space, execution, and delayed recall were significantly lower than those at baseline. These findings indicate that moxibustion improves patient’s attention and delayed recall. If not, visual space, execution, and delayed recall in MCI patients tend to be declined over time. No obvious adverse responses to moxibustion treatment occurred in the preliminary study. Discussion: This proposed trial has the potential to uncover that moxibustion will enhance cognitive-related brain function connections and effector brain networks, which is not yet known. If moxibustion is shown to be an effective and safe treatment strategy in patients with amnestic MCI, then this may pave the way for use of this treatment in clinic amnestic MCI. Ethics and dissemination: This study was approved by the Medical Ethics Committee of the Chinese Clinical Trial Registry (approval No. ChiECRCT-2017018) in October 2016, and registered on April 2017. The study was designed in June 2016. Patient recruitment was initialized in October 2016. Data analysis will be completed in December 2019. Results will be disseminated through publication in a peer-reviewed journal. Protocol version: 1.0. Trial registration: This trial was registered with the Chinese Clinical Trial Registry (registration number: ChiCTR-POC-17011162).

BACKGROUND Schizophrenia is a severe psychiatric disease, and its prevalence is higher. However, diagnosis of early-stage schizophrenia is still considered a challenging task. AIM To employ brain morphological features and machine learning method to differentiate male individuals with schizophrenia from healthy controls. METHODS The least absolute shrinkage and selection operator and t tests were applied to select important features from structural magnetic resonance images as input features for classification. Four commonly used machine learning algorithms, the general linear model, random forest (RF), k-nearest neighbors, and support vector machine algorithms, were used to develop the classification models. The performance of the classification models was evaluated according to the area under the receiver operating characteristic curve (AUC). RESULTS A total of 8 important features with significant differences between groups were considered as input features for the establishment of classification models based on the four machine learning algorithms. Compared to other machine learning algorithms, RF yielded better performance in the discrimination of male schizophrenic individuals from healthy controls, with an AUC of 0.886. CONCLUSION Our research suggests that brain morphological features can be used to improve the early diagnosis of schizophrenia in male patients.

The hippocampus is one of the earliest brain regions affected in Alzheimer's disease (AD) and tests of hippocampal function have the potential to detect AD in its earliest stages. Given that the hippocampus is critically involved in allocentric spatial memory, this study applied a short test of spatial memory, the 4 Mountains Test (4MT), to determine whether test performance can differentiate mild cognitive impairment (MCI) patients with and without CSF biomarker evidence of underlying AD and whether the test can distinguish patients with MCI and mild AD dementia when applied in different cultural settings. Healthy controls (HC), patients with MCI, and mild AD dementia were recruited from study sites in UK and Italy. Study numbers were: HC (UK 20, Italy 10), MCI (UK 21, Italy 14), and AD (UK 11, Italy 9). Nineteen UK MCI patients were grouped into CSF biomarker-positive (MCI+, n = 10) and biomarker-negative (MCI-, n = 9) subgroups. Behavioral data were correlated with hippocampal volume and cortical thickness of the precuneus and posterior cingulate gyrus. Spatial memory was impaired in both UK and Italy MCI and AD patients. Test performance additionally differentiated between MCI+ and MCI- subgroups (P = 0.001). A 4MT score of ≤8/15 was associated with 100% sensitivity and 90% specificity for detection of early AD (MCI+ and mild AD dementia) in the UK population, and with 100% sensitivity and 50% specificity for detection of MCI and AD in the Italy sample. 4MT performance correlated with hippocampal volume in the UK population and cortical thickness of the precuneus in both study populations. In conclusion, performance on a hippocampus-sensitive test of spatial memory differentiates MCI due to AD with high diagnostic sensitivity and specificity. The observation that similar diagnostic sensitivity was obtained in two separate study populations, allied to the scalability and usability of the test in community memory clinics, supports future application of the 4MT in the diagnosis of pre-dementia due to AD.

Elucidating distinct morphological atrophy patterns of Alzheimer’s disease (AD) and its prodromal stage, namely, mild cognitive impairment (MCI) helps to improve early diagnosis and medical intervention of AD. On that account, we aimed to obtain distinct patterns of voxel-wise morphological atrophy and its further perturbation on structural covariance network in AD and MCI compared with healthy controls (HCs). T1-weighted anatomical images of matched AD, MCI, and HCs were included in this study. Gray matter volume was obtained using voxel-based morphometry and compared among three groups. In addition, structural covariance network of identified brain regions exhibiting morphological difference was constructed and compared between pairs of three groups. Thus, patients with AD have a reduced hippocampal volume and an increased rate of atrophy compared with MCI and HCs. MCI exhibited a decreased trend in bilateral hippocampal volume compared with HCs and the accelerated right hippocampal atrophy rate than HCs. In AD, the hippocampus further exhibited increased structural covariance connected to reward related brain regions, including the anterior cingulate cortex, the putamen, the caudate, and the insula compared with HCs. In addition, the patients with AD exhibited increased structural covariance of left hippocampus with the bilateral insula, the inferior frontal gyrus, the superior temporal gyrus, and the cerebellum than MCI. These results reveal distinct patterns of morphological atrophy in AD and MCI, providing new insights into pathology of AD.