Background:The relationship between neighborhood-level socioeconomic disadvantage and brain health is an emerging area of research with critical implications for public health and clinical practice, yet its influence on brain structure remains unclear. Purpose:To investigate the epidemiological association between neighborhood-level socioeconomic disadvantage [Area Deprivation Index (ADI)] and morphometric neuroimaging variables in a consecutive, non-disease enriched patient population. Materials and Methods:This study, conducted at an academic medical center and associated community partners, used consecutive cross-sectional MRI neuroimaging data from 2,826 inpatient and outpatient individuals without radiological evidence of disease from January 2024 to June 2024. ADI, a geospatially determined index of neighborhood-level disadvantage, was calculated for each individual. Linear regressions tested the relationship between ADI and multiple morphometric variables: brain age gap (BAG; estimated - chronological BA), total brain tissue volume (TBV; total gray + white matter), five subcortical region volumes (hippocampus, thalamus, caudate, putamen, and nucleus accumbens) and four cortical region volumes [anterior cingulate cortex, posterior cingulate cortex, medial prefrontal cortex (MPFC), lateral PFC (LPFC)]. Volumetric measures were normalized to intracranial volume. Models controlled for age, sex, and total white matter hyperintensity volume (WMHV). Results:2,826 individuals (mean age, 52.7 ± 18.8 [standard deviation]; 1732 women) were evaluated. Residence in the 20% most disadvantaged neighborhoods was associated with a higher BAG (βs > 2.12, Ps < .01) and decreased TBV (βs < -5.12, Ps < .05). Additionally, increased WMHV was higher among those in the most disadvantaged neighborhoods (ts < -2.50, Ps < .05) and associated with lower volume in most regions. Interaction models showed increased negative associations between WMHV and volumes of the caudate, nucleus accumbens, and lateral prefrontal cortex among those in the most disadvantaged neighborhoods. Conclusions:Neighborhood disadvantage is associated with adverse brain morphometry, including higher BAG, lower TBV, and amplified vascular-related regional volume loss.

BackgroundAtrophy of the medial temporal lobes and deep gray matter, along with ventricular enlargement, are typical structural magnetic resonance imaging (MRI) findings in the brains of patients with Alzheimer's disease (AD). However, there are few twin studies on this subject.PurposeTo determine whether the visual rating method (VRM) and tensor-based morphometry (TBM) can detect the structural brain changes in monozygotic and dizygotic twin pairs discordant for memory performance.Material and MethodsA total of 12 monozygotic and 24 same-sex dizygotic twin pairs discordant for memory performance and 44 cognitively healthy non-twin volunteers were studied.ResultsSignificant within-twin pair differences in brain atrophy were detected in the medial temporal lobes and ventricular areas (both with VRM and TBM), and in deep gray matter structures (TBM only). When monozygotic and dizygotic twin pairs were analyzed separately, the differences were not statistically significant.ConclusionOur study yielded promising TBM results in distinguishing memory-discordant co-twins in temporal and deep gray matter atrophy. In addition, the findings confirm the assumption that AD affects twins in the same way, independent of genes. Studies with larger twin cohorts would better reveal possible differences between monozygotic and dizygotic twin pairs.

Abstract Background Idiopathic normal pressure hydrocephalus (iNPH) is characterized by a clinical triad of symptoms: abnormal gait, memory problems, and urinary incontinence. Neuroimaging plays a crucial role in diagnosing iNPH. However, current radiological markers, though indicative, are not definitive, suggesting the limited capacity of these indices to capture mechanisms associated with iNPH and the reversibility of the symptoms. Aims This study aims to (1) determine the geometric features of the lateral ventricles, (2) develop a quantitative method for three-dimensional analysis, and (3) test the ability to predict response to shunt surgery. By examining these features as potential diagnostic markers, this research seeks to enhance the understanding of morphometric characteristics in iNPH, thereby paving the way for improved patient selection for surgical intervention. Methods Our study contained 170 patients (95 shunt responders and 75 non-responders) from the Kuopio NPH registry. Our inclusion criteria required pre-surgery and one-year post-surgery symptom assessments alongside preoperative anatomical magnetic resonance imaging (MRI). Volumetric brain segmentations were performed using cNeuro software on T1-MRI images, followed by the generation of 3D lateral ventricle meshes for geometric feature extraction. The classification task employed the LogitNet machine learning model to analyze 27 geometric features. Model performance evaluation utilized repeated nested cross-validation (10 rounds) with five inner folds for parameter tuning and five outer folds for model evaluation. Additionally, we generated a ranking of feature importance based on the LogitNet L1 regularization coefficients. Results Our analysis revealed that LogitNet achieved an AUC of 0.661 (SD = 0.066) performance across 10 rounds of cross-validation in predicting the shunt surgery response. The most prominent feature contributing to the model’s prediction was asphericity. Conclusion Our analysis suggests that the proposed set of features, especially asphericity, effectively captures valuable information linked to the reversibility of iNPH.

ABSTRACT Background Fatigue is among the most common symptoms and one of the main factors determining the quality of life in multiple sclerosis (MS). However, the neurobiological mechanisms underlying fatigue are not fully understood. Here we studied lesion locations and their connections in individuals with MS, aiming to identify brain networks associated with fatigue. Methods 38 MS patients with and 21 without fatigue were included in the study. Association between fatigue and lesion locations was investigated using voxel‐lesion symptom mapping and lesion connectivity using lesion network mapping. The findings were tested in two independent datasets, including (1) MS patients scanned using resting‐state functional connectivity MRI (rs‐fcMRI) ( n = 199) and (2) individuals with stroke lesions ( n = 85). Results There were no specific anatomical MS lesion locations significantly associated with fatigue, but lesions associated with fatigue were connected to a common network with peak positive connectivity to the right premotor cortex and negative connectivity to the left temporal pole ( p FWE < 0.05). Of the two identified network nodes, connectivity from the premotor cortex to multiple other brain regions was significantly associated with MS fatigue severity in the independent dataset of MS patients ( p < 0.05). The MS fatigue network was also reproducible in poststroke fatigue (spatial correlation r = 0.57, permutation test p = 0.02), again showing that lesion connectivity to the premotor cortex, but not the temporal pole, was associated with fatigue ( p = 0.04). Conclusions Our results show that fatigue in MS localizes to a brain network, lending insight into the neural substrates of fatigue.

Accurate differential diagnosis of neurodegenerative diseases is challenging, but crucial for the management and treatment, particularly given the development of disease-modifying drug therapies for Alzheimer’s disease (AD). In this work, we investigate imaging biomarkers derived from T1-weighted magnetic resonance imaging (MRI) with a focus on differentiating Parkinson-plus syndromes from other relevant diagnostic groups in dementia and Parkinson’s disease (PD). MR scans from 1206 subjects and three cohorts were used: Parkinson’s Disease Biomarkers Program (PDBP), Amsterdam Dementia Cohort (ADC) and PredictND cohort (PND) (Table 1). The following imaging biomarkers were quantified using an automated image quantification tool: computed medial temporal lobe atrophy score (MTA), computed global cortical atrophy score (GCA) [1], anterior vs. posterior score (APS) [2], the midsagittal areas of the midbrain and pons and midbrain/pons ratio, the volumes of the cerebellum and putamen. Percentiles versus age, sex and head size normalized data were used to calculate sensitivity and specificity. As Parkinson-plus syndromes are quite rare, a cutoff for each marker was chosen to ensure high specificity and avoid false positives. Figure 1 shows boxplot visualizations for all investigated imaging biomarkers and diagnostic groups. Table 2 shows the performance in differential diagnosis. 1) The midbrain area best differentiated progressive supranuclear palsy (PSP) from all other diagnostic groups. 2) The combination of pons area and putamen volume best differentiated multiple system atrophy (MSA) from other PD forms. 3) The combinations of pons area and MTA best differentiated MSA from several dementia forms. Automatic MRI quantification of brain structures such as the midsagittal area of the midbrain and pons, putamen volume and MTA rating can help clinicians to differentiate between common neurodegenerative diseases like AD, vascular dementia and frontotemporal dementia and more Parkinson-related neurodegenerative diseases like MSA, PSP, and dementia with Lewy bodies. References : [1] Koikkalainen et al., European Radiology, 2019 [2] Bruun et al, NeuroImage Clinical, 2019

The increasing dementia prevalence and potential introduction of disease-modifying therapies (DMTs) highlight the need for efficient diagnostic pathways. Clear recommendations to guide the choice of diagnostic tests are lacking and may vary depending on different clinical scenarios. We used a data-driven approach to identify efficient and effective stepwise diagnostic testing for three clinical scenarios: 1) syndrome diagnosis, 2) etiological diagnosis, 3) potential eligibility for DMT. We used data from two memory clinic cohorts (ADC, PredictND), including 504 patients with dementia (302 Alzheimer’s disease, 107 frontotemporal dementia, 35 vascular dementia, 60 dementia with Lewy bodies), 191 patients with mild cognitive impairment, and 188 cognitively healthy controls (CN). Tests included digital cognitive screening (cCOG), neuropsychological and functional assessment (NP), MRI with automated quantification, and CSF biomarkers. Sequential testing followed a predetermined order (Figure 1). Subsequent tests were conducted if the diagnosis remained uncertain. Diagnostic certainty was ascertained through a data-driven clinical decision support system (CDSS) that generated a disease state index probability score (DSI, 0-1), indicating the probability of each diagnosis. Diagnosis was confirmed if the DSI exceeded a predefined threshold, set based on sensitivity/specificity cutoffs relevant for each clinical scenario and step. We assessed correct diagnoses and the need for additional testing at each step. For syndrome diagnosis, stepwise testing (cCOG, NP, MRI) accurately identified 71% of the patients, with NP needed in 42%, and MRI in 31%. For etiological diagnosis, starting with cognitive testing to rule out dementia resulted in the need for MRI in 84% of cases, including 91% of dementia patients and 25% CN. Subsequent MRI reduced CSF required to 29%, ultimately diagnosing 81% of patients with 71% accuracy. In determining DMT eligibility, stepwise testing (cCOG, NP, MRI) correctly identified 91% of potential eligible patients for confirmatory CSF testing, while only 51% of ineligible patients. Depending on the setting, alternative diagnostic pathways are accurate and efficient. As such, a data-driven tool can assist clinicians in selecting tests of added value across different clinical contexts. This becomes especially important with DMT availability, where the need for more efficient diagnostic pathways is crucial to maintain accessibility and affordability of diagnoses.

BackgroundThe usefulness of neurofilament light (NfL) as a biomarker for small vessel disease has not been established. We examined the relationship between NfL, neuroimaging changes, and clinical findings in subjects with varying degrees of white matter hyperintensity (WMH).MethodsA subgroup of participants (n = 35) in the Helsinki Small Vessel Disease Study underwent an analysis of NfL in cerebrospinal fluid (CSF) as well as brain magnetic resonance imaging (MRI) and neuropsychological and motor performance assessments. WMH and structural brain volumes were obtained with automatic segmentation.ResultsCSF NfL did not correlate significantly with total WMH volume (r = 0.278, p = 0.105). However, strong correlations were observed between CSF NfL and volumes of cerebral grey matter (r = −0.569, p < 0.001), cerebral cortex (r = −0.563, p < 0.001), and hippocampi (r = −0.492, p = 0.003). CSF NfL also correlated with composite measures of global cognition (r = −0.403, p = 0.016), executive functions (r = −0.402, p = 0.017), memory (r = −0.463, p = 0.005), and processing speed (r = −0.386, p = 0.022). Regarding motor performance, CSF NfL was correlated with Timed Up and Go (TUG) test (r = 0.531, p = 0.001), and gait speed (r = −0.450, p = 0.007), but not with single-leg stance. After adjusting for age, associations with volumes in MRI, functional mobility (TUG), and gait speed remained significant, whereas associations with cognitive performance attenuated below the significance level despite medium to large effect sizes.ConclusionNfL was strongly related to global gray matter and hippocampal atrophy, but not to WMH severity. NfL was also associated with motor performance. Our results suggest that NfL is independently associated with brain atrophy and functional mobility, but is not a reliable marker for cerebral small vessel disease.

BACKGROUND:The increasing prevalence of dementia and the introduction of disease-modifying therapies (DMTs) highlight the need for efficient diagnostic pathways in memory clinics. We present a data-driven approach to efficiently guide stepwise diagnostic testing for three clinical scenarios: 1) syndrome diagnosis, 2) etiological diagnosis, and 3) eligibility for DMT. METHODS:We used data from two memory clinic cohorts (ADC, PredictND), including 504 patients with dementia (302 Alzheimer's disease, 107 frontotemporal dementia, 35 vascular dementia, 60 dementia with Lewy bodies), 191 patients with mild cognitive impairment, and 188 cognitively normal controls (CN). Tests included digital cognitive screening (cCOG), neuropsychological and functional assessment (NP), MRI with automated quantification, and CSF biomarkers. Sequential testing followed a predetermined order, guided by diagnostic certainty. Diagnostic certainty was determined using a clinical decision support system (CDSS) that generates a disease state index (DSI, 0-1), indicating the probability of the syndrome diagnosis or underlying etiology. Diagnosis was confirmed if the DSI exceeded a predefined threshold based on sensitivity/specificity cutoffs relevant to each clinical scenario. Diagnostic accuracy and the need for additional testing were assessed at each step. RESULTS:Using cCOG as a prescreener for 1) syndrome diagnosis has the potential to accurately reduce the need for extensive NP (42%), resulting in syndrome diagnosis in all patients, with a diagnostic accuracy of 0.71, which was comparable to using NP alone. For 2) etiological diagnosis, stepwise testing resulted in an etiological diagnosis in 80% of patients with a diagnostic accuracy of 0.77, with MRI needed in 77%, and CSF in 37%. When 3) determining DMT eligibility, stepwise testing (100% cCOG, 83% NP, 75% MRI) selected 60% of the patients for confirmatory CSF testing and eventually identified 90% of the potentially eligible patients with AD dementia. CONCLUSIONS:Different diagnostic pathways are accurate and efficient depending on the setting. As such, a data-driven tool holds promise for assisting clinicians in selecting tests of added value across different clinical contexts. This becomes especially important with DMT availability, where the need for more efficient diagnostic pathways is crucial to maintain the accessibility and affordability of dementia diagnoses.

Introduction Subjective cognitive complaints are common in patients with cerebral small vessel disease (SVD), yet their correspondence to informant evaluations, objective cognitive functions and severity of brain changes are poorly understood. We studied the associations of subjective and informant reports of cognitive difficulties (executive functions and memory) with findings from a comprehensive neuropsychological assessment and brain MRI (white matter hyperintensities, WMH volume) as well as depressive symptoms and functional abilities (instrumental activities of daily living, IADL). Methods In the Helsinki SVD Study, 152 older adults with varying degrees of WMH but without stroke or dementia were classified as having normal cognition or mild cognitive impairment (MCI) based on Jak/Bondi neuropsychological criteria. The objective cognitive measures also included continuous domain scores for memory and executive functions. Cognitive complaints were evaluated with the subjective- and informant-versions of Prospective and Retrospective Memory Questionnaire (PRMQ) and Dysexecutive Questionnaire (DEX), functional abilities with the Amsterdam IADL Questionnaire and depressive symptoms with the Geriatric Depression Scale (GDS-15). Results Subjective cognitive complaints correlated significantly with informant reports (r=0.40-0.50, p<0.001). After controlling for age, gender and years of education, subjective and informant DEX and PRMQ were not related to MCI or cognitive domain scores (all p-values>0.05). Instead, subjective DEX (OR 1.10, CI 95% 1.05-1.16, p<0.001) and subjective PRMQ (OR 1.06, CI 95% 1.01-1.11, p=0.014) were significantly associated with GDS-15. Informant DEX (standardised β=0.26, p=0.002, f2=0.08) and informant PRMQ (standardised β=0.24, p=0.007, f2=0.06) were significantly related to WMH volume. They were also associated with IADL score (informant DEX, standardised β=-0.33, p=0.001, f2=0.30; informant PRMQ, standardised β=-0.24, p=0.011, f2=0.19). Discussion Neither subjective nor informant-reported cognitive complaints were associated with objective cognitive performance in terms of MCI categorisation or more sensitive domain scores of executive functioning or memory. Informant-evaluations were related to functional impairment in IADL and more severe WMH, whereas subjective complaints only associated with depressive symptoms. These findings suggest that awareness of cognitive impairment may be limited in early-stage SVD and highlight the value of informant assessments in the identification of patients with functional impairment.

When MRI images of patients with memory problems are assessed, the presence of lacunar infarcts is evaluated. Lacunar infarcts, fluid-filled cavities of 3-15 mm in diameter, are one possible result of cerebral small vessel disease and therefore relevant in dementia diagnostics. In this study, we propose an automated tool for detecting lacunar infarcts on MRI images. We adapted our previous approach [1] in three ways: 1) post-processing steps were added to the convolutional neural network segmentation pipeline, e.g., to exclude enlarged perivascular spaces and segmentations located inside sulci or ventricles, 2) memory clinic data from the Amsterdam Dementia Cohort and PredictND cohort were included in addition to the LADIS cohort data, and 3) the ground truth was defined by three experienced neuroradiologists (A, B, C) instead of one rater. T1 and T2-FLAIR images were available for all cases and T2 for a subset in visual rating. The dataset was composed of 393 subjects and each expert rated 1156 potential lacunar infarcts as “1 = no”, “2 = probably no”, “3 = probably yes” or “4 = yes”. A consensus (CO) was created by calculating the average and using a cutoff at 2.5. The agreement was measured between individual raters (A-B, B-C, A-C), between automatic rating and individual raters (AU-A, AU-B, AU-C), and between the automatic rating and the consensus rating (AU-CO) using Kappa. Correlation coefficient for the number of lacunar infarcts per subject was defined. Cross-validation was used in training the algorithm. Out of 1156 candidate regions evaluated, 540 were considered as lacunar infarcts based on consensus. The individual raters agreed with each other in 74% of cases whether the shown lesion was lacunar infarct (kappa = 0.48). Correspondingly, automatic rating agreed with the individual raters in 74% of cases (kappa = 0.47). When automatic rating was compared with consensus, agreement/accuracy was 78% (kappa = 0.55). The correlation coefficient for the number of lacunar infarcts detected automatically from the whole image and visually (consensus) was 0.85 (Figure 1). This study suggests that the agreement in detecting lacunar infarcts automatically is comparable to inter-rater variability between experienced neuroradiologists. [1] Jokinen et al. Stroke 2020 Nov 9; 50(1)

AbstractObjectives:Neuropsychiatric symptoms are related to disease progression and cognitive decline over time in cerebral small vessel disease (SVD) but their significance is poorly understood in covert SVD. We investigated neuropsychiatric symptoms and their relationships between cognitive and functional abilities in subjects with varying degrees of white matter hyperintensities (WMH), but without clinical diagnosis of stroke, dementia or significant disability.Methods:The Helsinki Small Vessel Disease Study consisted of 152 subjects, who underwent brain magnetic resonance imaging (MRI) and comprehensive neuropsychological evaluation of global cognition, processing speed, executive functions, and memory. Neuropsychiatric symptoms were evaluated with the Neuropsychiatric Inventory Questionnaire (NPI-Q, n = 134) and functional abilities with the Amsterdam Instrumental Activities of Daily Living questionnaire (A-IADL, n = 132), both filled in by a close informant.Results:NPI-Q total score correlated significantly with WMH volume (rs = 0.20, p = 0.019) and inversely with A-IADL score (rs = −0.41, p < 0.001). In total, 38% of the subjects had one or more informant-evaluated neuropsychiatric symptom. Linear regressions adjusted for age, sex, and education revealed no direct associations between neuropsychiatric symptoms and cognitive performance. However, there were significant synergistic interactions between neuropsychiatric symptoms and WMH volume on cognitive outcomes. Neuropsychiatric symptoms were also associated with A-IADL score irrespective of WMH volume.Conclusions:Neuropsychiatric symptoms are associated with an accelerated relationship between WMH and cognitive impairment. Furthermore, the presence of neuropsychiatric symptoms is related to worse functional abilities. Neuropsychiatric symptoms should be routinely assessed in covert SVD as they are related to worse cognitive and functional outcomes.

ObjectiveDepression is a common comorbidity in Parkinson's disease (PD) and other synucleinopathies. In non-PD geriatric patients, cortical atrophy has previously been connected to depression. Here, we investigated cortical atrophy and vascular white matter hyperintensities (WMHs) in autopsy-confirmed parkinsonism patients with the focus on clinical depression.MethodsThe sample consisted of 50 patients with a postmortem confirmed neuropathological diagnosis (30 Parkinson's disease [PD], 10 progressive supranuclear palsy [PSP] and 10 multiple system atrophy [MSA]). Each patient had been scanned with brain computerized tomography (CT) antemortem (median motor symptom duration at scanning = 3.0 years), and 19 patients were scanned again after a mean interval of 2.7 years. Medial temporal atrophy (MTA), global cortical atrophy (GCA) and WMHs were evaluated computationally from CT scans using an image quantification tool based on convolutional neural networks. Depression and other clinical parameters were recorded from patient files.ResultsDepression was associated with increased MTA after controlling for diagnosis, age, symptom duration, and cognition (p = 0.006). A similar finding was observed with GCA (p = 0.017) but not with WMH (p = 0.47). In PD patients alone, the result was confirmed for MTA (p = 0.021) with the same covariates. In the longitudinal analysis, GCA change per year was more severe in depressed patients than in nondepressed patients (p = 0.029).ConclusionsEarly medial temporal and global cortical atrophy, as detected with automated analysis of CT-images using convolutional neural networks, is associated with clinical depression in parkinsonism patients. Global cortical atrophy seems to progress faster in depressed patients.

Background The use of amyloid-PET in dementia workup is upcoming. At the same time, amyloid-PET is costly and limitedly available. While the appropriate use criteria (AUC) aim for optimal use of amyloid-PET, their limited sensitivity hinders the translation to clinical practice. Therefore, there is a need for tools that guide selection of patients for whom amyloid-PET has the most clinical utility. We aimed to develop a computerized decision support approach to select patients for amyloid-PET. Methods We included 286 subjects (135 controls, 108 Alzheimer’s disease dementia, 33 frontotemporal lobe dementia, and 10 vascular dementia) from the Amsterdam Dementia Cohort, with available neuropsychology, APOE, MRI and [18F]florbetaben amyloid-PET. In our computerized decision support approach, using supervised machine learning based on the DSI classifier, we first classified the subjects using only neuropsychology, APOE, and quantified MRI. Then, for subjects with uncertain classification (probability of correct class (PCC) < 0.75) we enriched classification by adding (hypothetical) amyloid positive (AD-like) and negative (normal) PET visual read results and assessed whether the diagnosis became more certain in at least one scenario (PPC≥0.75). If this was the case, the actual visual read result was used in the final classification. We compared the proportion of PET scans and patients diagnosed with sufficient certainty in the computerized approach with three scenarios: 1) without amyloid-PET, 2) amyloid-PET according to the AUC, and 3) amyloid-PET for all patients. Results The computerized approach advised PET in n = 60(21%) patients, leading to a diagnosis with sufficient certainty in n = 188(66%) patients. This approach was more efficient than the other three scenarios: 1) without amyloid-PET, diagnostic classification was obtained in n = 155(54%), 2) applying the AUC resulted in amyloid-PET in n = 113(40%) and diagnostic classification in n = 156(55%), and 3) performing amyloid-PET in all resulted in diagnostic classification in n = 154(54%). Conclusion Our computerized data-driven approach selected 21% of memory clinic patients for amyloid-PET, without compromising diagnostic performance. Our work contributes to a cost-effective implementation and could support clinicians in making a balanced decision in ordering additional amyloid PET during the dementia workup.

Objective:Subjective cognitive complaints are common in patients with cerebral small vessel disease (cSVD), yet their relationship with informant evaluations, objective cognitive functions and severity of brain changes are poorly understood. We studied the associations of subjective and informant reports with findings from comprehensive neuropsychological assessment and brain MRI.Method:In the Helsinki SVD Study, 152 older adults with varying degrees of white matter hyperintensities (WMH) but without stroke or dementia were classified as having normal cognition or mild cognitive impairment (MCI) based on neuropsychological criteria. The measures also included continuous domain scores for memory and executive functions. Cognitive complaints were evaluated with the subjective and informant versions of the Prospective and Retrospective Memory Questionnaire (PRMQ) and Dysexecutive Questionnaire (DEX); functional abilities with the Amsterdam Instrumental Activities of Daily Living Questionnaire (A-IADL); and depressive symptoms with the Geriatric Depression Scale (GDS-15).Results:Subjective cognitive complaints correlated significantly with informant reports (r=0.40-0.50, p<0.001). After controlling for demographics, subjective and informant DEX and PRMQ were not related to MCI, memory or executive functions. Instead, subjective DEX and PRMQ significantly associated with GDS-15 and informant DEX and PRMQ with WMH volume and A-IADL.Conclusions:Neither subjective nor informant-reported cognitive complaints associated with objective cognitive performance. Informant-evaluations were related to functional impairment and more severe WMH, whereas subjective complaints only associated with mild depressive symptoms. These findings suggest that awareness of cognitive impairment may be limited in early-stage cSVD and highlight the value of informant assessments in the identification of patients with functional impairment.

The use of amyloid-PET in daily clinical practice is upcoming. Yet, ttranslation of the appropriate use criteria (AUC) to clinical practice is challenging, hampering successful implementation of amyloid-PET. Tools that guide selecting patients for whom amyloid-PET has most clinical utility are needed. Therefore, we developed and evaluated a computerized decision support approach to select patients for amyloid-PET testing. We included 286 subjects (136 controls, 116 Alzheimer’s disease (AD) dementia, 26 frontotemporal lobe dementia (FTD), and 8 vascular dementia (VaD) from the Amsterdam Dementia Cohort, with available neuropsychology, APOE, MRI and [ 18 F]florbetaben amyloid-PET. In our computerized decision support approach, we first classified the subjects using only neuropsychology, APOE, and MRI data. Then, for uncertain subjects (probability of correct class (PPC) < 0.75) we tested the classification by adding amyloid positive (AD like) and negative (normal) visual PET read results, and assessed whether either of the values makes the diagnosis reliable (PPC≥0.75). If the threshold of PCC was reached, the actual visual PET read result was used in the final classification. We compared the proportion of PET scans and patients diagnosed with sufficient confidence (PPC ≥0.75) in the computerized approach with three scenarios: 1) without amyloid-PET (only demographics, neuropsychology, APOE and MRI), 2) amyloid-PET according to the AUC and 3) amyloid-PET for all patients. The study was performed using five-fold cross-validation. The computerized approach advised PET in n = 76(27%) patients, leading to a diagnosis with sufficient confidence in n = 190(66%) patients. This approach outperformed the other three scenarios: 1) without amyloid-PET, diagnosis was obtained in n = 147(51%), 2) applying the AUC resulted in amyloid-PET in n = 113(40%) and diagnosis in n = 160(56%), and 3) performing amyloid-PET in all patients resulted in diagnosis in n = 165(58%). Our computerized data-driven approach restricted the application of amyloid-PET to 27% in a memory clinic cohort of controls, AD, FTD, and VaD, without compromising diagnostic accuracy. This approach can support clinicians in making a balanced decision in ordering additional biomarker testing during the dementia workup. Thus, innovative use of resources can be stimulated, which is essential when disease-modifying drugs increase the demand for biomarker testing.

Abstract Background Brain atrophy appears during the progression of multiple sclerosis (MS) and is associated with the disability caused by the disease. Methods We investigated global and regional grey matter (GM) and white matter (WM) volumes, WM lesion load, and corpus callosum index (CCI), in benign relapsing‐remitting MS (BRRMS, n = 35) with and without any treatment and compared those to aggressive relapsing‐remitting MS (ARRMS, n = 46). Structures were analyzed by using an automated MRI quantification tool (cNeuro®). Results The total brain and cerebral WM volumes were larger in BRRMS than in ARRMS (p = .014, p = .017 respectively). In BRRMS, total brain volumes, regional GM volumes, and CCI were found similar whether or not disease‐modifying treatment (DMT) was used. The total (p = .033), as well as subcortical (p = .046) and deep WM (p = .041) lesion load volumes were larger in BRRMS patients without DMT. Cortical GM volumes did not differ between BRRMS and ARRMS, but the volumes of total brain tissue (p = .014) and thalami (p = .003) were larger in patients with BRRMS compared to ARRMS. A positive correlation was found between CCI and whole‐brain volume in both BRRMS (r = .73, p < .001) and ARRMS (r = .80, p < .01). Conclusions Thalamic volume is the most prominent measure to differentiate BRRMS and ARRMS. Validation of automated quantification of CCI provides an additional applicable MRI biomarker to detect brain atrophy in MS.

Dementia affects more and more people worldwide. Unfortunately, only half of dementia cases are well recognized. This indicates a need for both efficient and tailored tools for diagnosis. To improve the number of correctly diagnosed patients while retaining cost‐effectiveness, we study a stepwise data‐driven approach for the diagnostic workup. By using the online cognitive test tool cCOG [1] as prescreener, we aim to select the right diagnostic workup for each patient.

BACKGROUND:White matter hyperintensities (WMHs) are markers for cerebrovascular pathology, which are frequently seen in patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD). Verbal fluency is often impaired especially in AD, but little research has been conducted concerning the specific effects of WMH on verbal fluency in MCI and AD.OBJECTIVE:Our aim was to examine the relationship between WMH and verbal fluency in healthy old age and pathological aging (MCI/AD) using quantified MRI data.METHODS:Measures for semantic and phonemic fluency as well as quantified MRI imaging data from a sample of 42 cognitively healthy older adults and 44 patients with MCI/AD (total n = 86) were utilized. Analyses were performed both using the total sample that contained seven left-handed/ambidextrous participants, as well with a sample containing only right-handed participants (n = 79) in order to guard against possible confounding effects regarding language lateralization.RESULTS:After controlling for age and education and adjusting for multiple correction, WMH in the bilateral frontal and parieto-occipital areas as well as the right temporal area were associated with semantic fluency in cognitively healthy and MCI/AD patients but only in the models containing solely right-handed participants.CONCLUSION:The results indicate that white matter pathology in both frontal and parieto-occipital cerebral areas may have associations with impaired semantic fluency in right-handed older adults. However, elevated levels of WMH do not seem to be associated with cumulative effects on verbal fluency impairment in patients with MCI or AD. Further studies on the subject are needed.

PURPOSE:Automated analysis of neuroimaging data is commonly based on magnetic resonance imaging (MRI), but sometimes the availability is limited or a patient might have contradictions to MRI. Therefore, automated analyses of computed tomography (CT) images would be beneficial.METHODS:We developed an automated method to evaluate medial temporal lobe atrophy (MTA), global cortical atrophy (GCA), and the severity of white matter lesions (WMLs) from a CT scan and compared the results to those obtained from MRI in a cohort of 214 subjects gathered from Kuopio and Helsinki University Hospital registers from 2005 - 2016.RESULTS:The correlation coefficients of computational measures between CT and MRI were 0.9 (MTA), 0.82 (GCA), and 0.86 (Fazekas). CT-based measures were identical to MRI-based measures in 60% (MTA), 62% (GCA) and 60% (Fazekas) of cases when the measures were rounded to the nearest full grade variable. However, the difference in measures was 1 or less in 97-98% of cases. Similar results were obtained for cortical atrophy ratings, especially in the frontal and temporal lobes, when assessing the brain lobes separately. Bland-Altman plots and weighted kappa values demonstrated high agreement regarding measures based on CT and MRI.CONCLUSIONS:MTA, GCA, and Fazekas grades can also be assessed reliably from a CT scan with our method. Even though the measures obtained with the different imaging modalities were not identical in a relatively extensive cohort, the differences were minor. This expands the possibility of using this automated analysis method when MRI is inaccessible or contraindicated.