Neural Basis of Cognitive Assessment in Alzheimer Disease, Amnestic Mild Cognitive Impairment, and Subjective Memory Complaints

Data-driven prediction of regional brain metabolism using neuropsychological assessment in Alzheimer's disease and behavioral variant Frontotemporal dementia

BACKGROUND

This study aimed to evaluate the capacity of neuropsychological assessment to predict the regional brain metabolism in a cohort of patients with amnestic Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD) using Machine Learning algorithms.

METHODS

We included 360 subjects, consisting of 186 patients with AD, 87 with bvFTD, and 87 cognitively healthy controls. All participants underwent a neuropsychological assessment using the Addenbrooke's Cognitive Examination and the Neuronorma battery, in addition to [18F]-fluorodeoxyglucose positron emission tomography (FDG-PET) imaging. We trained Machine Learning algorithms, including artificial neural networks (ANN) and models that incorporate genetic algorithms (GAs), to predict the presence of regional hypometabolism in FDG-PET imaging based on cognitive testing results.

RESULTS

The proposed models demonstrated the ability to predict hypometabolism trends with approximately 70% accuracy in key regions associated with AD and bvFTD. In addition, we showed that incorporating neuropsychological tests provided relevant information for predicting brain hypometabolism. The temporal lobe was the best-predicted region, followed by the parietal, frontal, and some areas in the occipital lobe. Diagnosis played a significant role in the estimation of hypometabolism, and several neuropsychological tests were identified as the most important predictors for different brain regions. In our experiments, classical Machine Learning models, such as support vector machines enhanced by a preliminary feature selection step using GAs outperformed ANNs.

CONCLUSIONS

A successful prediction of regional brain metabolism of patients with AD and bvFTD was achieved based on the results of neuropsychological examination and Machine Learning algorithms. These findings support the neurobiological validity of neuropsychological examination and the feasibility of a topographical diagnosis in patients with neurodegenerative disorders.

Comparison of the Diagnostic Accuracy of Five Cognitive Screening Tests for Diagnosing Mild Cognitive Impairment in Patients Consulting for Memory Loss

Objectives: We aimed to evaluate and compare the diagnostic capacity of five cognitive screening tests for the diagnosis of mild cognitive impairment (MCI) in patients consulting by memory loss. Methods: A cross-sectional study involving 140 participants with a mean age of 74.42 ± 7.60 years, 87 (62.14%) women. Patients were classified as MCI or cognitively unimpaired according to a comprehensive neuropsychological battery. The diagnostic properties of the following screening tests were compared: Mini-Mental State Examination (MMSE), Addenbrooke's Cognitive Examination III (ACE-III) and Mini-Addenbrooke (M-ACE), Memory Impairment Screen (MIS), Montreal Cognitive Assessment (MoCA), and Rowland Universal Dementia Assessment Scale (RUDAS). Results: The area under the curve (AUC) was 0.861 for the ACE-III, 0.867 for M-ACE, 0.791 for MoCA, 0.795 for MMSE, 0.731 for RUDAS, and 0.672 for MIS. For the memory components, the AUC was 0.869 for ACE-III, 0.717 for MMSE, 0.755 for MoCA, and 0.720 for RUDAS. Cronbach's alpha was 0.827 for ACE-III, 0.505 for MMSE, 0.896 for MoCA, and 0.721 for RUDAS. Correlations with Free and Cued Selective Reminding Test were moderate with M-ACE, ACE-III, and MoCA, and moderate for the other tests. The M-ACE showed the best balance between diagnostic capacity and time of administration. Conclusions: ACE-III and its brief version M-ACE showed better diagnostic properties for the diagnosis of MCI than the other screening tests. MoCA and MMSE showed adequate properties, while the diagnostic capacity of MIS and RUDAS was limited.

Brain-wide functional connectivity alterations and their cognitive correlates in subjective cognitive decline

Background

Individuals with subjective cognitive decline (SCD) are at risk of developing Alzheimer's Disease (AD). Traditional seed-based analysis has shown biased functional connectivity (FC) in SCD individuals. To investigate unbiased altered FC by the brain-wide association study (BWAS) and to determine its association with cognition in SCD individuals.

Methods

Measure of association (MA) analysis was applied to detect significant voxels with FC changes. Based on these changes, we identified regions of interest (ROIs) and conducted ROI-wise FC analyses. Correlation analyses were then performed between these FC circuits and cognition.

Results

MA analysis identified 10 ROIs with significantly altered voxels. ROI-wise FC analyses revealed 14 strengthened FC, predominantly parietal-occipital link alterations. The FC between the right superior occipital gyrus and the right postcentral gyrus correlated positively with executive function, while the FC between the right middle occipital gyrus and the left angular gyrus correlated positively with episodic memory in SCD individuals.

Conclusion

SCD involves multifocal impairments, of which regions of default mode network (DMN) and occipital lobe should be specially focused. Cross-hemispheric alterations indicate an internal interactive impairment pattern in SCD. The reduced FC between the right superior occipital gyrus and the right postcentral gyrus, and between the right middle occipital gyrus and the left angular gyrus, which correlate with specific cognitive functions, could serve as potential biomarkers for SCD diagnosis.

Identifying Progression-Specific Alzheimer's Subtypes Using Multimodal Transformer

Alzheimer's disease (AD) is the most prevalent neurodegenerative disease, yet its current treatments are limited to stopping disease progression. Moreover, the effectiveness of these treatments remains uncertain due to the heterogeneity of the disease. Therefore, it is essential to identify disease subtypes at a very early stage. Current data-driven approaches can be used to classify subtypes during later stages of AD or related disorders, but making predictions in the asymptomatic or prodromal stage is challenging. Furthermore, the classifications of most existing models lack explainability, and these models rely solely on a single modality for assessment, limiting the scope of their analysis. Thus, we propose a multimodal framework that utilizes early-stage indicators, including imaging, genetics, and clinical assessments, to classify AD patients into progression-specific subtypes at an early stage. In our framework, we introduce a tri-modal co-attention mechanism (Tri-COAT) to explicitly capture cross-modal feature associations. Data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) (slow progressing = 177, intermediate = 302, and fast = 15) were used to train and evaluate Tri-COAT using a 10-fold stratified cross-testing approach. Our proposed model outperforms baseline models and sheds light on essential associations across multimodal features supported by known biological mechanisms. The multimodal design behind Tri-COAT allows it to achieve the highest classification area under the receiver operating characteristic curve while simultaneously providing interpretability to the model predictions through the co-attention mechanism.

Genetic modifiers of cognitive decline in PSEN1 E280A Alzheimer's disease

INTRODUCTION

Rate of cognitive decline (RCD) in Alzheimer's disease (AD) determines the degree of impairment for patients and of burden for caretakers. We studied the association of RCD with genetic variants in AD.

METHODS

RCD was evaluated in 62 familial AD (FAD) and 53 sporadic AD (SAD) cases, and analyzed by whole-exome sequencing for association with common exonic functional variants. Findings were validated in post mortem brain tissue.

RESULTS

One hundred seventy-two gene variants in FAD, and 227 gene variants in SAD associated with RCD. In FAD, performance decline of the immediate recall of the Rey-Osterrieth figure test associated with 122 genetic variants. Olfactory receptor OR51B6 showed the highest number of associated variants. Its expression was detected in temporal cortex neurons.

DISCUSSION

Impaired olfactory function has been associated with cognitive impairment in AD. Genetic variants in these or other genes could help to identify risk of faster memory decline in FAD and SAD patients.

Multimodal cross enhanced fusion network for diagnosis of Alzheimer's disease and subjective memory complaints

Deep learning methods using multimodal imagings have been proposed for the diagnosis of Alzheimer's disease (AD) and its early stages (SMC, subjective memory complaints), which may help to slow the progression of the disease through early intervention. However, current fusion methods for multimodal imagings are generally coarse and may lead to suboptimal results through the use of shared extractors or simple downscaling stitching. Another issue with diagnosing brain diseases is that they often affect multiple areas of the brain, making it important to consider potential connections throughout the brain. However, traditional convolutional neural networks (CNNs) may struggle with this issue due to their limited local receptive fields. To address this, many researchers have turned to transformer networks, which can provide global information about the brain but can be computationally intensive and perform poorly on small datasets. In this work, we propose a novel lightweight network called MENet that adaptively recalibrates the multiscale long-range receptive field to localize discriminative brain regions in a computationally efficient manner. Based on this, the network extracts the intensity and location responses between structural magnetic resonance imagings (sMRI) and 18-Fluoro-Deoxy-Glucose Positron Emission computed Tomography (FDG-PET) as an enhancement fusion for AD and SMC diagnosis. Our method is evaluated on the publicly available ADNI datasets and achieves 97.67% accuracy in AD diagnosis tasks and 81.63% accuracy in SMC diagnosis tasks using sMRI and FDG-PET. These results achieve state-of-the-art (SOTA) performance in both tasks. To the best of our knowledge, this is one of the first deep learning research methods for SMC diagnosis with FDG-PET.

Design and Verbal Fluency in Alzheimer's Disease and Frontotemporal Dementia: Clinical and Metabolic Correlates

OBJECTIVE

Cognitive processes underlying verbal and design fluency, and their neural correlates in patients with Alzheimer's disease (AD) and behavioural variant Frontotemporal Dementia (bvFTD) remain unclear. We hypothesised that verbal and design fluency may be associated with distinct neuropsychological processes in AD and FTD, showing different patterns of impairment and neural basis.

METHODS

We enrolled 142 participants including patients with AD (n = 80, mean age = 74.71), bvFTD (n = 34, mean age = 68.18), and healthy controls (HCs) (n = 28, mean age = 71.14), that underwent cognitive assessment and ¹⁸F-fluorodeoxyglucose positron emission tomography imaging.

RESULTS

Semantic and phonemic fluency showed the largest effect sizes between groups, showing lower scores in bvFTD than AD and HCs, and lower scores in AD than HC. Both AD and bvFTD showed a lower number of unique designs in design fluency in comparison to HC. Semantic fluency was correlated with left frontotemporal lobe in AD, and with left frontal, caudate, and thalamus in bvFTD. Percentage of unique designs in design fluency was associated with the metabolism of the bilateral fronto-temporo-parietal cortex in AD, and the bilateral frontal cortex with right predominance in bvFTD. Repetitions in AD were correlated with bilateral frontal, temporal, and parietal lobes, and with left prefrontal cortex in bvFTD.

CONCLUSIONS

Our findings demonstrate differential underlying cognitive processes in verbal and design fluency in AD and bvFTD. While memory and executive functioning associated with fronto-temporo-parietal regions were key in AD, attention and executive functions correlated with the frontal cortex and played a more significant role in bvFTD during fluency tasks.

GA-MADRID: design and validation of a machine learning tool for the diagnosis of Alzheimer’s disease and frontotemporal dementia using genetic algorithms

Artificial Intelligence aids early diagnosis and development of new treatments, which is key to slow down the progress of the diseases, which to date have no cure. The patients’ evaluation is carried out through diagnostic techniques such as clinical assessments neuroimaging techniques, which provide high-dimensionality data. In this work, a computational tool is presented that deals with the data provided by the clinical diagnostic techniques. This is a Python-based framework implemented with a modular design and fully extendable. It integrates (i) data processing and management of missing values and outliers; (ii) implementation of an evolutionary feature engineering approach, developed as a Python package, called PyWinEA using Mono-objective and Multi-objetive Genetic Algorithms (NSGAII); (iii) a module for designing predictive models based on a wide range of machine learning algorithms; (iv) a multiclass decision stage based on evolutionary grammars and Bayesian networks. Developed under the eXplainable Artificial Intelligence and open science perspective, this framework provides promising advances and opens the door to the understanding of neurodegenerative diseases from a data-centric point of view. In this work, we have successfully evaluated the potential of the framework for early and automated diagnosis with neuroimages and neurocognitive assessments from patients with Alzheimer’s disease (AD) and frontotemporal dementia (FTD).

Graphical abstract

Neural basis of visuospatial tests in behavioral variant frontotemporal dementia

Background

Recent models of visuospatial functioning suggest the existence of three main circuits emerging from the dorsal (“where”) route: parieto-prefrontal pathway, parieto-premotor, and parieto-medial temporal. Neural underpinnings of visuospatial task performance and the sparing of visuospatial functioning in bvFTD are unclear. We hypothesized different neural and cognitive mechanisms in visuospatial tasks performance in bvFTD and AD.

Methods

Two hundred and sixteen participants were enrolled for this study: 72 patients with bvFTD dementia and 144 patients with AD. Visual Object and Space Perception Battery Position Discrimination and Number Location (VOSP-PD and VOSP-NL) and Rey-Osterrieth Complex Figure (ROCF) were administered to examine visuospatial functioning, together with a comprehensive neuropsychological battery. FDG-PET was acquired to evaluate brain metabolism. Voxel-based brain mapping analyses were conducted to evaluate the brain regions associated with visuospatial function in bvFTD and AD.

Results

Patients with AD performed worst in visuospatial tasks in mild dementia, but not at prodromal stage. Attention and executive functioning tests showed higher correlations in bvFTD than AD with ROCF, but not VOSP subtests. Visuospatial performance in patients with bvFTD was associated with bilateral frontal regions, including the superior and medial frontal gyri, supplementary motor area, insula and middle cingulate gyrus.

Conclusion

These findings support the role of prefrontal and premotor regions in visuospatial processing through the connection with the posterior parietal cortex and other posterior cortical regions. Visuospatial deficits should be interpreted with caution in patients with bvFTD, and should not be regarded as hallmarks of posterior cortical dysfunction.

Neuropsychological, Metabolic, and Connectivity Underpinnings of Semantic Interference Deficits Using the LASSI-L

BACKGROUND

LASSI-L is a novel neuropsychological test specifically designed for the early diagnosis of Alzheimer's disease (AD) based on semantic interference.

OBJECTIVE

To examine the cognitive and neural underpinnings of the failure to recover from proactive semantic and retroactive semantic interference.

METHODS

One hundred and fifty-five patients consulting for memory loss were included. Patients underwent neuropsychological assessment, including the LASSI-L, and FDG-PET imaging. They were categorized as subjective memory complaints (SMC) (n=32), pre-mild cognitive impairment (MCI) due to AD (Pre-MCI) (n=39), MCI due to AD (MCI-AD) (n=71), and MCI without evidence of neurodegeneration (MCI-NN) (n=13). Voxel-based brain mapping and metabolic network connectivity analyses were conducted.

RESULTS

A significant group effect was found for all the LASSI-L scores. LASSI-L scores measuring failure to recover from proactive semantic interference and retroactive semantic interference were predicted by other neuropsychological tests with a precision of 64.1 and 44.8%. The LASSI-L scores were associated with brain metabolism in the bilateral precuneus, superior, middle and inferior temporal gyri, fusiform, angular, superior and inferior parietal lobule, superior, middle and inferior occipital gyri, lingual gyrus, and posterior cingulate. Connectivity analysis revealed a decrease of node degree and centrality in posterior cingulate in patients showing frPSI.

CONCLUSION

Episodic memory dysfunction and the involvement of the medial temporal lobe, precuneus and posterior cingulate constitute the basis of the failure to recover from proactive semantic interference and retroactive semantic interference. These findings support the role of the LASSI-L in the detection, monitoring and outcome prediction during the early stages of AD.

Planning in amnestic mild cognitive impairment: an fMRI study

INTRODUCTION

The memory impairment that is characteristic of amnestic mild cognitive impairment (aMCI) is often accompanied by difficulties in executive functioning, including planning. Though planning deficits in aMCI are well documented, their neural correlates are largely unknown, and have not yet been investigated with functional magnetic resonance imaging (fMRI).

OBJECTIVES

The aim of this study was to: (1) identify differences in brain activity and connectivity during planning in people with aMCI and cognitively healthy older adults, and (2) find whether planning-related activity and connectivity are associated with cognitive performance and symptoms of apathy.

METHODS

Twenty-five people with aMCI and 15 cognitively healthy older adults performed a visuospatial planning task (Tower of London; ToL) during fMRI. Task-related brain activation, spatial maps of task-related independent components, and seed-to-voxel functional connectivity were compared between the two groups and regressed against measures of executive functions (Trail Making Test difference score, TMT B-A; Digit Symbol Substitution Test, DSST), delayed recall (Rey Auditory Verbal Learning Test), and apathy (Apathy Evaluation Scale).

RESULTS

People with aMCI scored lower on task-switching (TMT B-A), working memory (DSST), and planning (ToL). During planning, people with aMCI had less activation in the bilateral anterior calcarine sulcus/cuneus, the bilateral temporal cortices, the left precentral gyrus, the thalamus, and the right cerebellum. Across all participants, higher planning-related activity in the supplementary motor area, the retrosplenial cortex and surrounding areas, and the right temporal cortex was related to better delayed recall. There were no between-group differences in functional connectivity, nor were there any associations between connectivity and cognition. We also did not find any associations between brain activity or connectivity and apathy.

CONCLUSION

Impaired planning in people with aMCI appears to be accompanied by lower activation in a diffuse cortico-thalamic network. Across all participants, higher planning-related activity in parieto-occipital, temporal, and frontal areas was related to better memory performance. The results point to the relevance of planning deficits for understanding aMCI and extend its clinical and neurobiological signature.

Fusion analysis of gray matter and white matter in subjective cognitive decline and mild cognitive impairment by multimodal CCA-joint ICA

BACKGROUND

Previous multimodal neuroimaging studies analyzed each dataset independently in subjective cognitive decline (SCD) and mild cognitive impairment (MCI), missing the cross-information. Multi-modal fusion analysis can provide more integral and comprehensive information regarding the brain. There has been a paucity of research on fusion analysis of sMRI and DTI in SCD and MCI.

MATERIALS AND METHODS

In the present study, we conducted fusion analysis of structural MRI and DTI by applying multimodal canonical correlation analysis with joint independent component analysis (mCCA-jICA) to capture the cross-information of gray matter (GM) and white matter (WM) in 62 SCD patients, 99 MCI patients, and 70 healthy controls (HCs). We further analyzed correlations between the mixing coefficients of mCCA-jICA and neuropsychological scores among the three groups.

RESULTS

A set of joint-discriminative independent components of GM and fractional anisotropy (FA) exhibited significant links between SCD and HCs, as well as between MCI and HCs. The covariant abnormalities primarily involved the frontal lobe/middle temporal gyrus/calcarine sulcus-anterior thalamic radiation/superior longitudinal fasciculus in SCD, and middle temporal gyrus/ fusiform gyrus/caudate necleus-forceps minor/anterior thalamic radiation in MCI. There was no significant difference between SCD and MCI groups.

CONCLUSIONS

The covariant GM-WM abnormalities in SCD and MCI were found in specific brain regions involved in cognitive processing, which confirms the simultaneous GM and WM changes underlying cognitive decline. These findings suggest that multimodal fusion analysis allows for a more comprehensive understanding of the association among different types of brain tissues and its crucial role in the neuropathological mechanism of SCD and MCI.

Glucose metabolism in the right middle temporal gyrus could be a potential biomarker for subjective cognitive decline: a study of a Han population

Introduction

Subjective cognitive decline (SCD) represents a cognitively normal state but at an increased risk for developing Alzheimer’s disease (AD). Recognizing the glucose metabolic biomarkers of SCD could facilitate the location of areas with metabolic changes at an ultra-early stage. The objective of this study was to explore glucose metabolic biomarkers of SCD at the region of interest (ROI) level.

Methods

This study was based on cohorts from two tertiary medical centers, and it was part of the SILCODE project (NCT03370744). Twenty-six normal control (NC) cases and 32 SCD cases were in cohort 1; 36 NCs, 23 cases of SCD, 32 cases of amnestic mild cognitive impairment (aMCIs), 32 cases of AD dementia (ADDs), and 22 cases of dementia with Lewy bodies (DLBs) were in cohort 2. Each subject underwent [18F]fluoro-2-deoxyglucose positron emission tomography (PET) imaging and magnetic resonance imaging (MRI), and subjects from cohort 1 additionally underwent amyloid-PET scanning. The ROI analysis was based on the Anatomical Automatic Labeling (AAL) template; multiple permutation tests and repeated cross-validations were conducted to determine the metabolic differences between NC and SCD cases. In addition, receiver operating characteristic curves were used to evaluate the capabilities of potential glucose metabolic biomarkers in distinguishing different groups. Pearson correlation analysis was also performed to explore the correlation between glucose metabolic biomarkers and neuropsychological scales or amyloid deposition.

Results

Only the right middle temporal gyrus (RMTG) passed the methodological verification, and its metabolic levels were correlated with the degrees of complaints (R = − 0.239, p = 0.009), depression (R = − 0.200, p = 0.030), and abilities of delayed memory (R = 0.207, p = 0.025), and were weakly correlated with cortical amyloid deposition (R = − 0.246, p = 0.066). Furthermore, RMTG metabolism gradually decreased across the cognitive continuum, and its diagnostic efficiency was comparable (NC vs. ADD, aMCI, or DLB) or even superior (NC vs. SCD) to that of the metabolism of the posterior cingulate cortex or precuneus.

Conclusions

These findings suggest that the hypometabolism of RMTG could be a typical feature of SCD, and the large-scale hypometabolism in patients with symptomatic stages of AD may start from the RMTG, which gradually progresses starting in the preclinical stage. The specificity of identifying SCD from the perspective of self-perceived symptoms is likely to be increased by the detection of RMTG metabolism.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13195-021-00811-w.

Neuroimaging advances regarding subjective cognitive decline in preclinical Alzheimer's disease

Subjective cognitive decline (SCD) is regarded as the first clinical manifestation in the Alzheimer’s disease (AD) continuum. Investigating populations with SCD is important for understanding the early pathological mechanisms of AD and identifying SCD-related biomarkers, which are critical for the early detection of AD. With the advent of advanced neuroimaging techniques, such as positron emission tomography (PET) and magnetic resonance imaging (MRI), accumulating evidence has revealed structural and functional brain alterations related to the symptoms of SCD. In this review, we summarize the main imaging features and key findings regarding SCD related to AD, from local and regional data to connectivity-based imaging measures, with the aim of delineating a multimodal imaging signature of SCD due to AD. Additionally, the interaction of SCD with other risk factors for dementia due to AD, such as age and the Apolipoprotein E (ApoE) ɛ4 status, has also been described. Finally, the possible explanations for the inconsistent and heterogeneous neuroimaging findings observed in individuals with SCD are discussed, along with future directions. Overall, the literature reveals a preferential vulnerability of AD signature regions in SCD in the context of AD, supporting the notion that individuals with SCD share a similar pattern of brain alterations with patients with mild cognitive impairment (MCI) and dementia due to AD. We conclude that these neuroimaging techniques, particularly multimodal neuroimaging techniques, have great potential for identifying the underlying pathological alterations associated with SCD. More longitudinal studies with larger sample sizes combined with more advanced imaging modeling approaches such as artificial intelligence are still warranted to establish their clinical utility.

Validation of the Neuronorma battery for neuropsychological assessment in multiple sclerosis

BACKGROUND

Cognitive dysfunction is prevalent among patients with multiple sclerosis (MS). In recent years, several (generally brief) neuropsychological batteries have been proposed for cognitive assessment. There is a need for comprehensive batteries providing complete cognitive assessment of patients with MS. The Neuronorma battery includes several standardised neuropsychological tests examining the main cognitive domains, and is available in several countries. The aim of this study was to validate the battery for cognitive assessment in a sample of patients with MS and healthy controls, and to find the most appropriate criteria for defining cognitive impairment using this battery.

METHODS

Five hundred and sixty participants (280 with MS and 280 controls matched for age, sex, and years of education) were included. Inter-group differences were calculated using the Mann-Whitney U test and effect sizes with Cohen's d. Several criteria for definition of cognitive impairment were evaluated, according to different cut-off points, and the number of tests and cognitive domains impaired. Receiver operating characteristic curves with 95% confidence intervals were estimated and they were compared using the DeLong method.

RESULTS

Patients with MS showed poorer performance in almost all cognitive tests, with large effect sizes for the Symbol Digit Modalities Test and Judgement of Line Orientation, and moderate effects for Digit Span Backward, the Corsi test, Trail Making Test, Free and Cued Selective Reminding Test, Rey-Osterrieth Complex Figure (recall), verbal fluency (P words), and the Stroop Color-Word Interference Test. The area under the curve was superior for classification by cognitive domain than for the mean scaled score of the tests or the number of tests showing impairment according to different cut-off points for the adjusted scaled scores.

CONCLUSIONS

Our study validates the Neuronorma battery for cognitive assessment of patients with MS. The battery is currently available in several countries with reliable normative data, and may be useful in both the clinical and the research settings when comprehensive neuropsychological examination is warranted.

Amyloid PET findings in multiple sclerosis are associated with cognitive decline at 18 months

OBJECTIVE

To study the clinical, cognitive, and radiological progression of a cohort of patients with MS, taking into account the amyloid PET with 18F-florbetaben analyses.

METHODS

Twenty-nine patients with MS were assessed with longitudinal structural MRI and a clinical and comprehensive neuropsychological protocol, with a mean interval between assessments of 18 ± 3.31 months. 18F-florbetaben PET was performed at baseline. Uptake was analysed in demyelinating plaques (DWM) and normal-appearing white matter (NAWM). Results were correlated with clinical, cognitive and MRI data.

RESULTS

Patients with cognitive decline over the follow-up period showed a lower standardised uptake value ratio in NAWM and lower thalamic volume and a higher lesion load in the baseline MRI. Myelin status was correlated with EDSS and cognitive tests mainly evaluating visuospatial function and working memory. Lower uptake in NAWM at baseline was also associated with a growth in white matter lesion volume over time.

CONCLUSIONS

Lower white matter uptake in amyloid PET is associated with cognitive decline and an increase in white matter lesion volume during the follow-up. Our study suggests that 18F-florbetaben may be a useful biomarker in assessing myelin status in MS, understanding MS pathophysiology, and predicting cognitive outcomes.

Inhibition impairment in frontotemporal dementia, amyotrophic lateral sclerosis, and Alzheimer's disease: clinical assessment and metabolic correlates

The ability to reject an automatic tendency, i.e. inhibition, has been linked to the prefrontal cortex, but its neural underpinnings are still controversial. Neurodegenerative diseases represent an interesting model to explore this issue, given its frequent impairment in these disorders. We investigated the inhibitory impairment and its neural basis using four different tests, which evaluate the presence of inhibitory dysfunction (Stroop test, Hayling test, and two graphical perseveration tests), and assessed their correlation with brain metabolism using ¹⁸F-fluorodeoxyglucose positron emission tomography in a group of 76 participants with behavioral variant frontotemporal dementia (bvFTD), Alzheimer's disease (AD), amyotrophic lateral sclerosis (ALS) and healthy controls (HC). Inhibition impairment was more frequent in bvFTD and AD, than ALS and HC. AD and bvFTD only differed in the strategy used in Hayling test, and the frequency of impairment in graphical perseveration tests. Correlation between inhibition tests was moderate. The Stroop test correlated with several regions of the frontal and parietal lobes, mainly on the left side. Hayling test correlated with almost all regions of the frontal lobe and, especially, with the orbitofrontal cortex. Some differences in the impaired regions in each disease were found. Inhibition ability was mainly impaired in bvFTD and AD, and it correlated with the bilateral frontal lobe metabolism. There were certain particularities according to the specific task and patients evaluated. These dissimilarities may support the concept of inhibition as a multidimensional construct, with the involvement of common and divergent neural mechanisms.

Biomarker-Based Signature of Alzheimer's Disease in Pre-MCI Individuals

Alzheimer’s disease (AD) pathology begins decades before the onset of clinical symptoms. It is recognized as a clinicobiological entity, being detectable in vivo independently of the clinical stage by means of pathophysiological biomarkers. Accordingly, neuropathological studies that were carried out on healthy elderly subjects, with or without subjective experience of cognitive decline, reported evidence of AD pathology in a high proportion of cases. At present, mild cognitive impairment (MCI) represents the only clinically diagnosed pre-dementia stage. Several attempts have been carried out to detect AD as early as possible, when subtle cognitive alterations, still not fulfilling MCI criteria, appear. Importantly, pre-MCI individuals showing the positivity of pathophysiological AD biomarkers show a risk of progression similar to MCI patients. In view of successful treatment with disease modifying agents, in a clinical setting, a timely diagnosis is mandatory. In clinical routine, biomarkers assessment should be taken into consideration whenever a subject with subtle cognitive deficits (pre-MCI), who is aware of his/her decline, requests to know the cause of such disturbances. In this review, we report the available neuropsychological and biomarkers data that characterize the pre-MCI patients, thus proposing pre-MCI as the first clinical manifestation of AD.

Structural MRI correlates of PASAT performance in multiple sclerosis

Background

The Paced Auditory Serial Addition Test (PASAT) is a useful cognitive test in patients with multiple sclerosis (MS), assessing sustained attention and information processing speed. However, the neural underpinnings of performance in the test are controversial. We aimed to study the neural basis of PASAT performance by using structural magnetic resonance imaging (MRI) in a series of 242 patients with MS.

Methods

PASAT (3-s) was administered together with a comprehensive neuropsychological battery. Global brain volumes and total T2-weighted lesion volumes were estimated. Voxel-based morphometry and lesion symptom mapping analyses were performed.

Results

Mean PASAT score was 42.98 ± 10.44; results indicated impairment in 75 cases (31.0%). PASAT score was correlated with several clusters involving the following regions: bilateral precuneus and posterior cingulate, bilateral caudate and putamen, and bilateral cerebellum. Voxel-based lesion symptom mapping showed no significant clusters. Region of interest–based analysis restricted to white matter regions revealed a correlation with the left cingulum, corpus callosum, bilateral corticospinal tracts, and right arcuate fasciculus. Correlations between PASAT scores and global volumes were weak.

Conclusion

PASAT score was associated with regional volumes of the posterior cingulate/precuneus and several subcortical structures, specifically the caudate, putamen, and cerebellum. This emphasises the role of both cortical and subcortical structures in cognitive functioning and information processing speed in patients with MS.

Electronic supplementary material

The online version of this article (10.1186/s12883-018-1223-0) contains supplementary material, which is available to authorized users.

Identification of Cortical and Subcortical Correlates of Cognitive Performance in Multiple Sclerosis Using Voxel-Based Morphometry

Objective: Cognitive impairment is an important feature in multiple sclerosis (MS) and has been associated to several Magnetic Resonance Imaging (MRI) markers, but especially brain atrophy. However, the relationship between specific neuropsychological tests examining several cognitive functions and brain volumes has been little explored. Furthermore, because MS frequently damage subcortical regions, it may be an interesting model to examine the role of subcortical areas in cognitive functioning. Our aim was to identify correlations between specific brain regions and performance in neuropsychological tests evaluating different cognitive functions in a large series of patients with MS.

Methods: A total of 375 patients were evaluated with a comprehensive neuropsychological battery and with MRI. Voxel-based morphometry was conducted to analyse the correlation between cognitive performance and gray matter damage, using Statistical Parametric Mapping with the toolboxes VBM8 and Lesion Segmentation Tool.

Results: The following correlations were found: Corsi block-tapping test with right insula; Trail Making Test with caudate nucleus, thalamus, and several cortical regions including the posterior cingulate and inferior frontal gyrus; Symbol Digit Modalities Test with caudate nucleus, thalamus, posterior cingulate, several frontal regions, insula, and cerebellum; Stroop Color and Word Test with caudate nucleus and putamen; Free and Cued Selective Reminding Test and Rey-Osterrieth Complex Figure with thalamus, precuneus, and parahippocampal gyrus; Boston Naming Test with thalamus, caudate nucleus, and hippocampus; semantic verbal fluency with thalamus and phonological verbal fluency with caudate nucleus; and Tower of London test with frontal lobe, caudate nucleus, and posterior cingulate.

Conclusion: Our study provides valuable data on the cortical and subcortical basis of cognitive function in MS. Neuropsychological tests mainly assessing attention and executive function showed a stronger association with caudate volume, while tests primarily evaluating memory were more strongly correlated with the thalamus. Other relevant regions were the posterior cingulate/precuneus, which were associated with attentional tasks, and several frontal regions, which were found to be correlated with planning and higher order executive functioning. Furthermore, our study supports the brain vertical organization of cognitive functioning, with the participation of the cortex, thalamus, basal ganglia, and cerebellum.

Driving Ability in Alzheimer Disease Spectrum: Neural Basis, Assessment, and Potential Use of Optic Flow Event-Related Potentials

Driving requires multiple cognitive functions including visuospatial perception and recruits widespread brain networks. Recently, traffic accidents in dementia, particularly in Alzheimer disease spectrum (ADS), have increased and become an urgent social problem. Therefore, it is necessary to develop the objective and reliable biomarkers for driving ability in patients with ADS. Interestingly, even in the early stage of the disease, patients with ADS are characterized by the impairment of visuospatial function such as radial optic flow (OF) perception related to self-motion perception. For the last decade, we have studied the feasibility of event-related potentials (ERPs) in response to radial OF in ADS and proposed that OF-ERPs provided an additional information on the alteration of visuospatial perception in ADS (1, 2). Hence, we hypothesized that OF-ERPs can be a possible predictive biomarker of driving ability in ADS. In this review, the recent concept of neural substrates of driving in healthy humans are firstly outlined. Second, we mention the alterations of driving performance and its brain network in ADS. Third, the current status of assessment tools for driving ability is stated. Fourth, we describe ERP studies related to driving ability in ADS. Further, the neural basis of OF processing and OF-ERPs in healthy humans are mentioned. Finally, the application of OF-ERPs to ADS is described. The aim of this review was to introduce the potential use of OF-ERPs for assessment of driving ability in ADS.

Comparison between FCSRT and LASSI-L to Detect Early Stage Alzheimer's Disease

BACKGROUND

The Free and Cued Selective Reminding Test (FCSRT) is the most accurate test for the diagnosis of prodromal Alzheimer's disease (AD). Recently, a novel cognitive test, the Loewenstein-Acevedo Scale for Semantic Interference and Learning (LASSI-L), has been developed in order to provide an early diagnosis.

OBJECTIVE

To compare the diagnostic accuracy of the FCSRT and the LASSI-L for the diagnosis of AD in its preclinical and prodromal stages using 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) as a reference.

METHODS

Fifty patients consulting for subjective memory complaints without functional impairment and at risk for AD were enrolled and evaluated using FCSRT, LASSI-L, and FDG-PET. Participants were evaluated using a comprehensive neurological and neuropsychological protocol and were assessed with the FCSRT and LASSI-L. FDG-PET was acquired concomitantly and used for classification of patients as AD or non-AD according to brain metabolism using both visual and semi-quantitative methods.

RESULTS

LASSI-L scores allowed a better classification of patients as AD/non-AD in comparison to FCSRT. Logistic regression analysis showed delayed recall and failure to recovery from proactive semantic interference from LASSI-L as independent statistically significant predictors, obtaining an area under the curve of 0.894. This area under the curve provided a better discrimination than the best FCSRT score (total delayed recall, area under the curve 0.708, p = 0.029).

CONCLUSIONS

The LASSI-L, a cognitive stress test, was superior to FCSRT in the prediction of AD features on FDG-PET. This emphasizes the possibility to advance toward an earlier diagnosis of AD from a clinical perspective.

The Efficacy of Cognitive Intervention in Mild Cognitive Impairment (MCI): a Meta-Analysis of Outcomes on Neuropsychological Measures

Cognitive training in MCI may stimulate pre-existing neural reserves or recruit neural circuitry as “compensatory scaffolding” prompting neuroplastic reorganization to meet task demands (Reuter-Lorenz & Park, 2014). However, existing systematic reviews and meta-analytic studies exploring the benefits of cognitive interventions in MCI have been mixed. An updated examination regarding the efficacy of cognitive intervention in MCI is needed given improvements in adherence to MCI diagnostic criteria in subject selection, better defined interventions and strategies applied, increased use of neuropsychological measures pre- and post-intervention, as well as identification of moderator variables which may influence treatment. As such, this meta-analytic review was conducted to examine the efficacy of cognitive intervention in individuals diagnosed with mild cognitive impairment (MCI) versus MCI controls based on performance of neuropsychological outcome measures in randomized controlled trials (RCT). RCT studies published from January 1995 to June 2017 were obtained through source databases of MEDLINE-R, PubMed, Healthstar, Global Health, PSYCH-INFO, and Health and Psychological Instruments using search parameters for MCI diagnostic category (mild cognitive impairment, MCI, pre-Alzheimer’s disease, early cognitive decline, early onset Alzheimer’s disease, and preclinical Alzheimer’s disease) and the intervention or training conducted (intervention, training, stimulation, rehabilitation, or treatment). Other inclusion and exclusion criteria included subject selection based on established MCI criteria, RCT design in an outpatient setting, MCI controls (active or passive), and outcomes based on objective neuropsychological measures. From the 1199 abstracts identified, 26 articles met inclusion criteria for the meta-analyses completed across eleven (11) countries; 92.31% of which have been published within the past 7 years. A series of meta-analyses were performed to examine the effects of cognitive intervention by cognitive domain, type of training, and intervention content (cognitive domain targeted). We found significant, moderate effects for multicomponent training (Hedges’ g observed = 0.398; CI [0.164, 0.631]; Z = 3.337; p = 0.001; Q = 55.511; df = 15; p = 0.000; I² = 72.978%; τ² = 0.146) as well as multidomain-focused strategies (Hedges’ g = 0.230; 95% CI [0.108, 0.352]; Z = 3.692; p  < 0.001; Q = 12.713; df = 12; p = 0.390; I² = 5.612; τ² = 0.003). The effects for other interventions explored by cognitive domain, training type, or intervention content were indeterminate due to concerns for heterogeneity, bias, and small cell sizes. In addition, subgroup and meta-regression analyses were conducted with the moderators of MCI category, mode of intervention, training type, intervention content, program duration (total hours), type of control group (active or passive), post-intervention follow-up assessment period, and control for repeat administration. We found significant overall effects for intervention content with memory focused interventions appearing to be more effective than multidomain approaches. There was no evidence of an influence on outcomes for the other covariates examined. Overall, these findings suggest individuals with MCI who received multicomponent training or interventions targeting multiple domains (including lifestyle changes) were apt to display an improvement on outcome measures of cognition post-intervention. As such, multicomponent and multidomain forms of intervention may prompt recruitment of alternate neural processes as well as support primary networks to meet task demands simultaneously. In addition, interventions with memory and multidomain forms of content appear to be particularly helpful, with memory-based approaches possibly being more effective than multidomain methods. Other factors, such as program duration, appear to have less of an influence on intervention outcomes. Given this, although the creation of new primary network paths appears strained in MCI, interventions with memory-based or multidomain forms of content may facilitate partial activation of compensatory scaffolding and neuroplastic reorganization. The positive benefit of memory-based strategies may also reflect transfer effects indicative of compensatory network activation and the multiple-pathways involved in memory processes. Limitations of this review are similar to other meta-analysis in MCI, including a modest number studies, small sample sizes, multiple forms of interventions and types of training applied (some overlapping), and, while greatly improved in our view, a large diversity of instruments used to measure outcome. This is apt to have contributed to the presence of heterogeneity and publication bias precluding a more definitive determination of the outcomes observed.