Neuronal population codes are increasingly being investigated with multivariate pattern-information analyses. A key challenge is to use measured brain-activity patterns to test computational models of brain information processing. One approach to this problem is representational similarity analysis (RSA), which characterizes a representation in a brain or computational model by the distance matrix of the response patterns elicited by a set of stimuli. The representational distance matrix encapsulates what distinctions between stimuli are emphasized and what distinctions are de-emphasized in the representation. A model is tested by comparing the representational distance matrix it predicts to that of a measured brain region. RSA also enables us to compare representations between stages of processing within a given brain or model, between brain and behavioral data, and between individuals and species. Here, we introduce a Matlab toolbox for RSA. The toolbox supports an analysis approach that is simultaneously data- and hypothesis-driven. It is designed to help integrate a wide range of computational models into the analysis of multichannel brain-activity measurements as provided by modern functional imaging and neuronal recording techniques. Tools for visualization and inference enable the user to relate sets of models to sets of brain regions and to statistically test and compare the models using nonparametric inference methods. The toolbox supports searchlight-based RSA, to continuously map a measured brain volume in search of a neuronal population code with a specific geometry. Finally, we introduce the linear-discriminant t value as a measure of representational discriminability that bridges the gap between linear decoding analyses and RSA. In order to demonstrate the capabilities of the toolbox, we apply it to both simulated and real fMRI data. The key functions are equally applicable to other modalities of brain-activity measurement. The toolbox is freely available to the community under an open-source license agreement (http://www.mrc-cbu.cam.ac.uk/methods-and-resources/toolboxes/license/).

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 theApolipoprotein 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.

Mild cognitive impairment in Parkinson’s disease is associated with progression to dementia (Parkinson’s disease dementia) in a majority of patients. Determining structural imaging biomarkers associated with prodromal Parkinson’s disease dementia may allow for the earlier identification of those at risk, and allow for targeted disease modifying therapies. One hundred and five non-demented subjects with newly diagnosed idiopathic Parkinson’s disease and 37 healthy matched controls had serial 3 T structural magnetic resonance imaging scans with clinical and neuropsychological assessments at baseline, which were repeated after 18 months. The Movement Disorder Society Task Force criteria were used to classify the Parkinson’s disease subjects into Parkinson’s disease with mild cognitive impairment (n = 39) and Parkinson’s disease with no cognitive impairment (n = 66). Freesurfer image processing software was used to measure cortical thickness and subcortical volumes at baseline and follow-up. We compared regional percentage change of cortical thinning and subcortical atrophy over 18 months. At baseline, cases with Parkinson’s disease with mild cognitive impairment demonstrated widespread cortical thinning relative to controls and atrophy of the nucleus accumbens compared to both controls and subjects with Parkinson’s disease with no cognitive impairment. Regional cortical thickness at baseline was correlated with global cognition in the combined Parkinson’s disease cohort. Over 18 months, patients with Parkinson’s disease with mild cognitive impairment demonstrated more severe cortical thinning in frontal and temporo-parietal cortices, including hippocampal atrophy, relative to those with Parkinson’s disease and no cognitive impairment and healthy controls, whereas subjects with Parkinson’s disease and no cognitive impairment showed more severe frontal cortical thinning compared to healthy controls. At baseline, Parkinson’s disease with no cognitive impairment converters showed bilateral temporal cortex thinning relative to the Parkinson’s disease with no cognitive impairment stable subjects. Although loss of both cortical and subcortical volume occurs in non-demented Parkinson’s disease, our longitudinal analyses revealed that Parkinson’s disease with mild cognitive impairment shows more extensive atrophy and greater percentage of cortical thinning compared to Parkinson’s disease with no cognitive impairment. In particular, an extension of cortical thinning in the temporo-parietal regions in addition to frontal atrophy could be a biomarker in therapeutic studies of mild cognitive impairment in Parkinson’s disease for progression towards dementia.

Tau pathology, neuroinflammation, and neurodegeneration are key aspects of Alzheimer’s disease. Understanding whether these features predict cognitive decline, alone or in combination, is crucial to develop new prognostic measures and enhanced stratification for clinical trials. Here, we studied how baseline assessments of in vivo tau pathology (measured by ¹⁸F-AV-1451 PET), neuroinflammation (measured by ¹¹C-PK11195 PET) and brain atrophy (derived from structural MRI) predicted longitudinal cognitive changes in patients with Alzheimer’s disease pathology. Twenty-six patients (n = 12 with clinically probable Alzheimer’s dementia and n = 14 with amyloid-positive mild cognitive impairment) and 29 healthy control subjects underwent baseline assessment with ¹⁸F-AV-1451 PET, ¹¹C-PK11195 PET, and structural MRI. Cognition was examined annually over the subsequent 3 years using the revised Addenbrooke’s Cognitive Examination. Regional grey matter volumes, and regional binding of ¹⁸F-AV-1451 and ¹¹C-PK11195 were derived from 15 temporo-parietal regions characteristically affected by Alzheimer’s disease pathology. A principal component analysis was used on each imaging modality separately, to identify the main spatial distributions of pathology. A latent growth curve model was applied across the whole sample on longitudinal cognitive scores to estimate the rate of annual decline in each participant. We regressed the individuals’ estimated rate of cognitive decline on the neuroimaging components and examined univariable predictive models with single-modality predictors, and a multi-modality predictive model, to identify the independent and combined prognostic value of the different neuroimaging markers. Principal component analysis identified a single component for the grey matter atrophy, while two components were found for each PET ligand: one weighted to the anterior temporal lobe, and another weighted to posterior temporo-parietal regions. Across the whole-sample, the single-modality models indicated significant correlations between the rate of cognitive decline and the first component of each imaging modality. In patients, both stepwise backward elimination and Bayesian model selection revealed an optimal predictive model that included both components of ¹⁸F-AV-1451 and the first (i.e. anterior temporal) component for ¹¹C-PK11195. However, the MRI-derived atrophy component and demographic variables were excluded from the optimal predictive model of cognitive decline. We conclude that temporo-parietal tau pathology and anterior temporal neuroinflammation predict cognitive decline in patients with symptomatic Alzheimer’s disease pathology. This indicates the added value of PET biomarkers in predicting cognitive decline in Alzheimer’s disease, over and above MRI measures of brain atrophy and demographic data. Our findings also support the strategy for targeting tau and neuroinflammation in disease-modifying therapy against Alzheimer’s disease.

Inflammation is increasingly recognized as part of the pathology of neurodegenerative conditions such as Alzheimer’s disease and Parkinson’s disease, but its role in dementia with Lewy bodies remains unclear. Using multimodal imaging and peripheral cytokine analysis, we therefore investigated central and peripheral inflammation in this common form of dementia. Nineteen participants with probable dementia with Lewy bodies and 16 similarly aged controls underwent 3 T MRI and PET imaging with ¹¹C-PK11195, a marker of microglial activation in vivo. Peripheral blood inflammatory cytokines were also measured in all subjects, as well as in an additional 10 controls, using the Mesoscale Human Cytokine 36 plex panel and additional assays for high sensitivity c-reactive protein, tumour necrosis factor receptor 1, IL-34, YKL-40 (chitinase-3-like protein 1) and colony stimulating factor 1. To test for the presence of in vivo amyloid, ¹¹C-Pittsburgh compound B PET imaging was also performed in 16 of the dementia with Lewy body participants. Microglial activation was elevated in dementia with Lewy bodies subjects with mild disease when compared to those with moderate/severe impairment, where disease severity was indexed by cognitive performance on the revised Addenbrooke’s Cognitive Examination. In patients, strong correlations were found between cognitive performance and ¹¹C-PK11195 non-displaceable binding potential in several regions including the caudate nucleus (R = 0.83, P = 0.00008) and cuneus (R = 0.77, P = 0.0005). Several inflammatory cytokines were altered in the patients compared to controls, with elevated macrophage inflammatory protein-3 (P = 0.001), IL-17A (P = 0.008) and IL-2 (P = 0.046) and reduced IL-8 (P = 0.024). There was no correlation between cortical ¹¹C-Pittsburgh compound B standardized uptake value ratio and clinical features, regional ¹¹C-PK11195 binding or peripheral cytokine levels. Nor was there any regional correlation between ¹¹C-PK11195 non-displaceable binding potentials and ¹¹C-Pittsburgh compound B standardized uptake value ratios. Our findings provide evidence for both central and peripheral inflammatory changes in dementia with Lewy bodies, with microglial activation occurring early in the disease in key regions known to be associated with pathology, before declining as cognition declines. Raised peripheral cytokines associated with T cell function further suggest a role for the adaptive immune system in the pathogenesis of the disease.

The ability to assess the distribution and extent of tau pathology in Alzheimer’s disease and progressive supranuclear palsy in vivo would help to develop biomarkers for these tauopathies and clinical trials of disease-modifying therapies. New radioligands for positron emission tomography have generated considerable interest, and controversy, in their potential as tau biomarkers. We assessed the radiotracer ¹⁸F-AV-1451 with positron emission tomography imaging to compare the distribution and intensity of tau pathology in 15 patients with Alzheimer’s pathology (including amyloid-positive mild cognitive impairment), 19 patients with progressive supranuclear palsy, and 13 age- and sex-matched controls. Regional analysis of variance and a support vector machine were used to compare and discriminate the clinical groups, respectively. We also examined the ¹⁸F-AV-1451 autoradiographic binding in post-mortem tissue from patients with Alzheimer’s disease, progressive supranuclear palsy, and a control case to assess the ¹⁸F-AV-1451 binding specificity to Alzheimer’s and non-Alzheimer’s tau pathology. There was increased ¹⁸F-AV-1451 binding in multiple regions in living patients with Alzheimer’s disease and progressive supranuclear palsy relative to controls [main effect of group, F(2,41) = 17.5, P < 0.0001; region of interest × group interaction, F(2,68) = 7.5, P < 0.00001]. More specifically, ¹⁸F-AV-1451 binding was significantly increased in patients with Alzheimer’s disease, relative to patients with progressive supranuclear palsy and with control subjects, in the hippocampus and in occipital, parietal, temporal, and frontal cortices (t’s > 2.2, P’s < 0.04). Conversely, in patients with progressive supranuclear palsy, relative to patients with Alzheimer’s disease, ¹⁸F-AV-1451 binding was elevated in the midbrain (t = 2.1, P < 0.04); while patients with progressive supranuclear palsy showed, relative to controls, increased ¹⁸F-AV-1451 uptake in the putamen, pallidum, thalamus, midbrain, and in the dentate nucleus of the cerebellum (t’s > 2.7, P’s < 0.02). The support vector machine assigned patients’ diagnoses with 94% accuracy. The post-mortem autoradiographic data showed that ¹⁸F-AV-1451 strongly bound to Alzheimer-related tau pathology, but less specifically in progressive supranuclear palsy. ¹⁸F-AV-1451 binding to the basal ganglia was strong in all groups in vivo. Postmortem histochemical staining showed absence of neuromelanin-containing cells in the basal ganglia, indicating that off-target binding to neuromelanin is an insufficient explanation of ¹⁸F-AV-1451 positron emission tomography data in vivo, at least in the basal ganglia. Overall, we confirm the potential of ¹⁸F-AV-1451 as a heuristic biomarker, but caution is indicated in the neuropathological interpretation of its binding. Off-target binding may contribute to disease profiles of ¹⁸F-AV-1451 positron emission tomography, especially in primary tauopathies such as progressive supranuclear palsy. We suggest that ¹⁸F-AV-1451 positron emission tomography is a useful biomarker to assess tau pathology in Alzheimer’s disease and to distinguish it from other tauopathies with distinct clinical and pathological characteristics such as progressive supranuclear palsy.