The anterior and posterior hippocampal functional connectivity in Alzheimer’s disease and mild cognitive impairment

Introduction: The hippocampus has been an important region of interest in the study of Alzheimer’s Disease. It is intrinsically complex and has been divided into an anterior affective and a posterior cognitive role. Here, we assess the functional connectivity of the anterior and posterior hippocampal networks in AD and its prodromal clinical stage, Mild Cognitive Impairment (MCI).Methods: Using resting-state functional Magnetic Resonance Imaging and a seed-based approach, we generated the functional networks of the hippocampal extremities across 56 controls, 48 early MCI, 35 late MCI and 31 AD patients from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). We then compared the whole-brain networks between diagnosis groups, as well as measured the overlap between the hippocampus subregions networks.Results: Whereas the anterior hippocampus is functionally connected to the thalamus and the cingulate cortex, the posterior region remains localized within the temporal lobe. AD patients suffer disruption between the anterior hippocampus and the PCC, the contralateral anterior and middle hippocampus, superior parietal lobule, and the entorhinal cortex. In contrast, the functional connectivity of the posterior hippocampus in AD is disrupted with the contralateral anterior occipital gyrus, ipsilateral superior temporal gyrus, bilateral middle hippocampus and bilateral fasciolar gyrus. lMCI patients have comparable connectivity decreases within both networks but exhibit smaller clusters. Neither network shows decline in eMCI’s stage compared to controls. A conjunction analysis shows a decrease in intrinsic connectivity within the hippocampus as early as in eMCI. When assessing individual volumes, the loss of intrinsic connectivity is driven by the disruption of the anterior hippocampal network.Discussion: Our results support the dichotomous model of the hippocampus, as both anterior and posterior hippocampal networks are disrupted in lMCI and AD, but not in eMCI. The overlap between the two hippocampus networks decreases as the disease progresses and is driven by a functional decline of the anterior hippocampus. Further studies with increased resolution and discrimination between hippocampal subfields are warranted.Introduction: L’hippocampe est une importante région d’intérêt dans l’étude de la maladie d’Alzheimer (AD). Cette région est très complexe et peut être divisé entre une région antérieure affective et une postérieur qui joue un rôle cognitif. Dans ce qui suit, nous évaluons la connectivité fonctionnelle des réseaux de l’hippocampus antérieur et postérieur dans la AD et son étape prodrome clinique, le Déficit Cognitive Léger (MCI).Méthodes: En employant l’imagerie par resonance magnétique fonctionnelle est des régions d’intérêt a priori, nous avons généré les réseaux fonctionnelles des extrémités de l’hippocampe pour 56 contrôles, 48 MCI tôt, 35 MCI tard et 31 patients AD de l’Alzheimer’s Disease Neuroimaging Initiative (ADNI). Nous avons comparé les réseaux entre groupes de patients, et avons mesuré le chevauchement entre les réseaux des sub-régions de l’hippocampe.Résultats : Alors que l’hippocampe antérieur est connecté fonctionnement au thalamus et cortex cingulaire, la région postérieure reste plutôt localisé dans le lobe temporal. Les patients AD souffrent d’une perturbation de l’hippocampe antérieur et du cortex postérieur cingulaire, l’antérieur controlatéral, le lobe supérieur pariétal and le cortex entorhinal. En revanche, la connectivité fonctionnelle de l’hippocampe postérieur de l’AD est perturbé avec le gyrus occipital antérieur controlatéral, le gyrus temporal supérieur ipsilatéral, l’hippocampe du milieu bilatéral et le gyrus fasciolar bilatéral. Quant aux patients MCI tardives, ils présentent des changements motifs de connectivité comparables mais des grappes plus petites. Aucun des réseaux ne présente un déclin chez les patients MCI tôt. Une analyse de chevauchement démontre un déclin de la connectivité intrinsèque dès le MCI tôt. En mesurant les volumes individuels, la perte de connectivité intrinsèque est dû à la perturbation du réseau de l’hippocampe antérieur.Discussion: Nos résultats supportent le modèle dichotomique de l’hippocampe, alors que les réseaux antérieurs et postérieurs sont distinctivement affecté en MCI tard et AD, mais pas en MCI tôt. Nous recommandons davantage d’études de réseaux fonctionnels avec une plus grande résolution et discrimination des sous-régions de l’hippocampe

Common Effects of Amnestic Mild Cognitive Impairment on Resting-State Connectivity Across Four Independent Studies

Resting-state functional connectivity is a promising biomarker for Alzheimer's disease. However, previous resting-state functional magnetic resonance imaging studies in Alzheimer's disease and amnestic mild cognitive impairment (aMCI) have shown limited reproducibility as they have had small sample sizes and substantial variation in study protocol. We sought to identify functional brain networks and connections that could consistently discriminate normal aging from aMCI despite variations in scanner manufacturer, imaging protocol, and diagnostic procedure. We therefore combined four datasets collected independently, including 112 healthy controls and 143 patients with aMCI. We systematically tested multiple brain connections for associations with aMCI using a weighted average routinely used in meta-analyses. The largest effects involved the superior medial frontal cortex (including the anterior cingulate), dorsomedial prefrontal cortex, striatum, and middle temporal lobe. Compared with controls, patients with aMCI exhibited significantly decreased connectivity between default mode network nodes and between regions of the cortico-striatal-thalamic loop. Despite the heterogeneity of methods among the four datasets, we identified common aMCI-related connectivity changes with small to medium effect sizes and sample size estimates recommending a minimum of 140 to upwards of 600 total subjects to achieve adequate statistical power in the context of a multisite study with 5–10 scanning sites and about 10 subjects per group and per site. If our findings can be replicated and associated with other established biomarkers of Alzheimer's disease (e.g., amyloid and tau quantification), then these functional connections may be promising candidate biomarkers for Alzheimer's disease

Group functional template generated with BASC on cognitively normal elderly and mild cognitive impairment populations

<p><b>Content</b></p> <p>This work is derived from the Alzheimer's Disease Neuroimaging Initiative 2 (ADNI2) and three samples from Montreal, Canada, as described in the following publications : Tam et al 2015, (<a href="https://dx.doi.org/10.3389/fnagi.2015.00242">https://dx.doi.org/10.3389/fnagi.2015.00242</a>) ; Tam et al 2016, (<a href="https://doi.org/10.1016/j.dib.2016.11.036">https://doi.org/10.1016/j.dib.2016.11.036</a>). It includes group brain parcellations for clusters generated from resting-state functional magnetic resonance images for 99 cognitively normal elderly persons and 129 patients with mild cognitive impairment. The parcellations have been generated using a method called bootstrap analysis of stable clusters (BASC, Bellec et al., 2010) and 8 resolutions of clusters (4, 6, 12, 22, 33, 65, 111, and 208 total bihemispheric parcels) were selected using a data-driven method called MSTEPS (Bellec, 2013). This work also includes parcellations that contain regions-of-interest (ROIs) that span only one hemisphere at 8 resolutions (10, 17, 30, 51, 77, 137, 199, and 322 total ROIs). It also includes maps illustrating uncorrected functional connectivity differences (t-maps) between patients and controls for four seeds/ROIs (superior medial frontal cortex, dorsomedial prefrontal cortex, striatum, middle temporal lobe). This release contains the following files:</p> <ul> <li><b></b><b>README.md:</b> a text description of the release.</li> <li><b></b><b>brain_parcellation_mcinet_basc_(sym,asym)_(#)clusters.(mnc,nii).gz:</b> 3D volumes (either in .mnc or .nii format) at 3 mm isotropic resolution, in the MNI non-linear 2009a space (<a href="http://www.bic.mni.mcgill.ca/ServicesAtlases/ICBM152NLin2009">http://www.bic.mni.mcgill.ca/ServicesAtlases/ICBM152NLin2009</a>), at multiple resolutions of # clusters. Note that four versions of the templates are available, named with sym_mnc, asym_mnc, sym_nii or asym_nii. The mnc flavor contains files in the minc format, while the nii flavor has files in the nifti format. The asym flavor contains brain images that have been registered in the asymmetric version of the MNI brain template (reflecting that the brain is asymmetric), while with the sym flavor they have been registered in the symmetric version of the MNI template. The symmetric template has been forced to be symmetric anatomically, and is therefore ideally suited to study homotopic functional connections in fMRI: finding homotopic regions simply consists of flipping the x-axis of the template. Note: These clusters are often bihemispheric. For parcellations containing regions that span only one hemisphere, see below.</li> <li><b></b><b>brain_parcellation_mcinet_basc_(sym,asym)_(#)rois.(mnc,nii).gz:</b> 3D volumes (either in .mnc or .nii format) at 3 mm isotropic resolution, in the MNI non-linear 2009a space, at multiple resolutions of # ROIs, that span only one hemisphere. As above, mnc/nii and sym/asym versions of the templates are available. These spatially constrained region-level parcellations were derived from the cluster-level parcellations, as follows:</li> <ul> <li>4 clusters = 10 ROIs</li> <li>6 clusters = 17 ROIs</li> <li>12 clusters = 30 ROIs</li> <li>22 clusters = 51 ROIs</li> <li>33 clusters = 77 ROIs</li> <li>65 clusters = 137 ROIs</li> <li>111 clusters = 199 ROIs</li> <li>208 clusters = 322 ROIs</li> </ul> <li><b></b><b>labels_mcinet_(sym,asym)_ (#)(clusters,ROIs).csv:</b> spreadsheets containing labels for each cluster or ROI for resolutions containing 30 or more ROIs. For the resolution containing 12 clusters (or 30 ROIs), we manually labeled each cluster as follows:</li> <ul> <li>DGMN: deep gray matter nuclei,</li> <li>pDMN: posterior default mode network</li> <li>mTL: medial temporal lobe</li> <li>vTL: ventral temporal lobe</li> <li>dTL: dorsal temporal lobe</li> <li>aDMN: anterior default mode network</li> <li>OFC: orbitofrontal cortex</li> <li>pATT: posterior attention</li> <li>CER: cerebellum</li> <li>SM: sensory-motor</li> <li>VIS: visual</li> <li>FPN: frontoparietal network.</li> </ul> <li>Then, we decomposed the networks into smaller subclusters at all higher resolutions. Each parcel at higher resolutions was labeled in reference to the parcels at resolution 12, with the following convention: (resolution)(parcel label)(#); for example, at resolution (R) 22, the anterior default mode splits into two clusters, which were named “R22_aDMN_1” and “R22_aDMN_2”.</li> <li><b></b><b>ttest_ctrlvsmci_seed(#).(mnc,nii).gz:</b> 3D volumes (either in .mnc or .nii) displaying functional connectivity differences (uncorrected t-tests) between patients with mild cognitive impairment and cognitively normal elderly, for 4 different seeds/regions of interest i.e. striatum (seed #2), dorsomedial prefrontal cortex (#9), middle temporal lobe (#12), superior medial frontal cortex (#28); cluster numbers are taken from the parcellation containing 33 clusters.</li> </ul> <p><b></b><br></p> <p><b>Preprocessing</b></p> <p>The datasets were analysed using the NeuroImaging Analysis Kit (NIAK <a href="https://github.com/SIMEXP/niak">https://github.com/SIMEXP/niak</a>) version 0.12.18, under CentOS version 6.3 with Octave (<a href="http://gnu.octave.org/">http://gnu.octave.org</a>) version 3.8.1 and the Minc toolkit (<a href="http://www.bic.mni.mcgill.ca/ServicesSoftware/ServicesSoftwareMincToolKit">http://www.bic.mni.mcgill.ca/ServicesSoftware/ServicesSoftwareMincToolKit</a>) version 0.3.18. Brain parcellations were derived using BASC (Bellec et al. 2010). Functional connectomes were generated, and general linear models were used to test for differences between patients and controls for each connection between two clusters. Please see the README.md for more details.</p> <p><b></b><br></p> <p><b>References</b></p><ul> <li>Bellec, P, et al, 2010. Multi-level bootstrap analysis of stable clusters in resting-state fMRI. NeuroImage 51 (3), 1126–1139.</li> <li>Bellec, P, Jun. 2013. Mining the Hierarchy of Resting-State Brain Networks: Selection of Representative Clusters in a Multiscale Structure. In: Pattern Recognition in Neuroimaging (PRNI), 2013 International Workshop on. pp. 54–57.</li> <li>Tam, A, et al, 2015. Common Effects of Amnestic Mild Cognitive Impairment on Resting-State Connectivity Across Four Independent Studies. Frontiers in Aging Neuroscience 7, 242.</li> <li>Tam, A, et al, 2016. A dataset of multiresolution functional brain parcellations in an elderly population with no or mild cognitive impairment. Data in Brief 9, 1122–1129.</li> </ul><p><b></b><br></p><p><b>Other derivatives</b></p><p>The datasets that were used to generate the parcellations are described in a publication, see the following link: <a href="https://github.com/SIMEXP/mcinet">https://github.com/SIMEXP/mcinet</a></p

Test-retest resting-state fMRI in healthy elderly persons with a family history of Alzheimer’s disease

We present a test-retest dataset of resting-state fMRI data obtained in 80 cognitively normal elderly volunteers enrolled in the “Pre-symptomatic Evaluation of Novel or Experimental Treatments for Alzheimer's Disease” (PREVENT-AD) Cohort. Subjects with a family history of Alzheimer's disease in first-degree relatives were recruited as part of an on-going double blind randomized clinical trial of Naproxen or placebo. Two pairs of scans were acquired ~3 months apart, allowing the assessment of both intra- and inter-session reliability, with the possible caveat of treatment effects as a source of inter-session variation. Using the NeuroImaging Analysis Kit (NIAK), we report on the standard quality of co-registration and motion parameters of the data, and assess their validity based on the spatial distribution of seed-based connectivity maps as well as intra- and inter-session reliability metrics in the default-mode network. This resource, released publicly as sample UM1 of the Consortium for Reliability and Reproducibility (CoRR), will benefit future studies focusing on the preclinical period preceding the appearance of dementia in Alzheimer's disease

The neurophysiological brain-fingerprint of Parkinson’s diseaseResearch in context

Summary: Background: Research in healthy young adults shows that characteristic patterns of brain activity define individual “brain-fingerprints” that are unique to each person. However, variability in these brain-fingerprints increases in individuals with neurological conditions, challenging the clinical relevance and potential impact of the approach. Our study shows that brain-fingerprints derived from neurophysiological brain activity are associated with pathophysiological and clinical traits of individual patients with Parkinson’s disease (PD). Methods: We created brain-fingerprints from task-free brain activity recorded through magnetoencephalography in 79 PD patients and compared them with those from two independent samples of age-matched healthy controls (N = 424 total). We decomposed brain activity into arrhythmic and rhythmic components, defining distinct brain-fingerprints for each type from recording durations of up to 4 min and as short as 30 s. Findings: The arrhythmic spectral components of cortical activity in patients with Parkinson’s disease are more variable over short periods, challenging the definition of a reliable brain-fingerprint. However, by isolating the rhythmic components of cortical activity, we derived brain-fingerprints that distinguished between patients and healthy controls with about 90% accuracy. The most prominent cortical features of the resulting Parkinson’s brain-fingerprint are mapped to polyrhythmic activity in unimodal sensorimotor regions. Leveraging these features, we also demonstrate that Parkinson’s symptom laterality can be decoded directly from cortical neurophysiological activity. Furthermore, our study reveals that the cortical topography of the Parkinson’s brain-fingerprint aligns with that of neurotransmitter systems affected by the disease’s pathophysiology. Interpretation: The increased moment-to-moment variability of arrhythmic brain-fingerprints challenges patient differentiation and explains previously published results. We outline patient-specific rhythmic brain signaling features that provide insights into both the neurophysiological signature and symptom laterality of Parkinson’s disease. Thus, the proposed definition of a rhythmic brain-fingerprint of Parkinson’s disease may contribute to novel, refined approaches to patient stratification. Symmetrically, we discuss how rhythmic brain-fingerprints may contribute to the improved identification and testing of therapeutic neurostimulation targets. Funding: Data collection and sharing for this project was provided by the Quebec Parkinson Network (QPN), the Pre-symptomatic Evaluation of Novel or Experimental Treatments for Alzheimer’s Disease (PREVENT-AD; release 6.0) program, the Cambridge Centre for Aging Neuroscience (Cam-CAN), and the Open MEG Archives (OMEGA). The QPN is funded by a grant from Fonds de Recherche du Québec - Santé (FRQS). PREVENT-AD was launched in 2011 as a $13.5 million, 7-year public-private partnership using funds provided by McGill University, the FRQS, an unrestricted research grant from Pfizer Canada, the Levesque Foundation, the Douglas Hospital Research Centre and Foundation, the Government of Canada, and the Canada Fund for Innovation. The Brainstorm project is supported by funding to SB from the NIH (R01-EB026299-05). Further funding to SB for this study included a Discovery grant from the Natural Sciences and Engineering Research Council of Canada of Canada (436355-13), and the CIHR Canada research Chair in Neural Dynamics of Brain Systems (CRC-2017-00311)