201
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Ferreira D, Pereira JB, Volpe G, Westman E. Subtypes of Alzheimer's Disease Display Distinct Network Abnormalities Extending Beyond Their Pattern of Brain Atrophy. Front Neurol 2019; 10:524. [PMID: 31191430 PMCID: PMC6547836 DOI: 10.3389/fneur.2019.00524] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2019] [Accepted: 05/01/2019] [Indexed: 01/08/2023] Open
Abstract
Different subtypes of Alzheimer's disease (AD) with characteristic distributions of neurofibrillary tangles and corresponding brain atrophy patterns have been identified using structural magnetic resonance imaging (MRI). However, the underlying biological mechanisms that determine this differential expression of neurofibrillary tangles are still unknown. Here, we applied graph theoretical analysis to structural MRI data to test the hypothesis that differential network disruption is at the basis of the emergence of these AD subtypes. We studied a total of 175 AD patients and 81 controls. Subtyping was done using the Scheltens' scale for medial temporal lobe atrophy, the Koedam's scale for posterior atrophy, and the Pasquier's global cortical atrophy scale for frontal atrophy. A total of 89 AD patients showed a brain atrophy pattern consistent with typical AD; 30 patients showed a limbic-predominant pattern; 29 patients showed a hippocampal-sparing pattern; and 27 showed minimal atrophy. We built brain structural networks from 68 cortical regions and 14 subcortical gray matter structures for each AD subtype and for the controls, and we compared between-group measures of integration, segregation, and modular organization. At the global level, modularity was increased and differential modular reorganization was detected in the four subtypes. We also found a decrease of transitivity in the typical and hippocampal-sparing subtypes, as well as an increase of average local efficiency in the minimal atrophy and hippocampal-sparing subtypes. We conclude that the AD subtypes have a distinct signature of network disruption associated with their atrophy patterns and further extending to other brain regions, presumably reflecting the differential spread of neurofibrillary tangles. We discuss the hypothetical emergence of these subtypes and possible clinical implications.
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Affiliation(s)
- Daniel Ferreira
- Division of Clinical Geriatrics, Centre for Alzheimer Research, Department of Neurobiology, Care Sciences, and Society, Karolinska Institutet, Stockholm, Sweden
| | - Joana B Pereira
- Division of Clinical Geriatrics, Centre for Alzheimer Research, Department of Neurobiology, Care Sciences, and Society, Karolinska Institutet, Stockholm, Sweden
| | - Giovanni Volpe
- Department of Physics, University of Gothenburg, Gothenburg, Sweden
| | - Eric Westman
- Division of Clinical Geriatrics, Centre for Alzheimer Research, Department of Neurobiology, Care Sciences, and Society, Karolinska Institutet, Stockholm, Sweden.,Department of Neuroimaging, Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, United Kingdom
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202
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203
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Abstract
Tau tangles are a pathological hallmark of Alzheimer?s disease (AD) with strong correlations existing between tau aggregation and cognitive decline. Studies in mouse models have shown that the characteristic patterns of tau spatial spread associated with AD progression are determined by neural connectivity rather than physical proximity between different brain regions. We present here a network diffusion model for tau aggregation based on longitudinal tau measures from positron emission tomography (PET) and structural connectivity graphs from diffusion tensor imaging (DTI). White matter fiber bundles reconstructed via tractography from the DTI data were used to compute normalized graph Laplacians which served as graph diffusion kernels for tau spread. By linearizing this model and using sparse source localization, we were able to identify distinct patterns of propagative and generative buildup of tau at a population level. A gradient descent approach was used to solve the sparsity-constrained optimization problem. Model fitting was performed on subjects from the Harvard Aging Brain Study cohort. The fitted model parameters include a scalar factor controlling the network-based tau spread and a network-independent seed vector representing seeding in different regions-of-interest. This parametric model was validated on an independent group of subjects from the same cohort. We were able to predict with reasonably high accuracy the tau buildup at a future time-point. The network diffusion model, therefore, successfully identifies two distinct mechanisms for tau buildup in the aging brain and offers a macroscopic perspective on tau spread.
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204
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Pandya S, Zeighami Y, Freeze B, Dadar M, Collins DL, Dagher A, Raj A. Predictive model of spread of Parkinson's pathology using network diffusion. Neuroimage 2019; 192:178-194. [PMID: 30851444 PMCID: PMC7180066 DOI: 10.1016/j.neuroimage.2019.03.001] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2018] [Revised: 01/20/2019] [Accepted: 03/01/2019] [Indexed: 02/03/2023] Open
Abstract
Growing evidence suggests that a "prion-like" mechanism underlies the pathogenesis of many neurodegenerative disorders, including Parkinson's disease (PD). We extend and tailor previously developed quantitative and predictive network diffusion model (NDM) to PD, by specifically modeling the trans-neuronal spread of alpha-synuclein outward from the substantia nigra (SN). The model demonstrated the spatial and temporal patterns of PD from neuropathological and neuroimaging studies and was statistically validated using MRI deformation of 232 Parkinson's patients. After repeated seeding simulations, the SN was found to be the most likely seed region, supporting its unique lynchpin role in Parkinson's pathology spread. Other alternative spread models were also evaluated for comparison, specifically, random spread and distance-based spread; the latter tests for Braak's original caudorostral transmission theory. We showed that the distance-based spread model is not as well supported as the connectivity-based model. Intriguingly, the temporal sequencing of affected regions predicted by the model was in close agreement with Braak stages III-VI, providing what we consider a "computational Braak" staging system. Finally, we investigated whether the regional expression patterns of implicated genes contribute to regional atrophy. Despite robust evidence for genetic factors in PD pathogenesis, NDM outperformed regional genetic expression predictors, suggesting that network processes are far stronger mediators of regional vulnerability than innate or cell-autonomous factors. This is the first finding yet of the ramification of prion-like pathology propagation in Parkinson's, as gleaned from in vivo human imaging data. The NDM is potentially a promising robust and clinically useful tool for diagnosis, prognosis and staging of PD.
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Affiliation(s)
- S Pandya
- Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA.
| | - Y Zeighami
- Montreal Neurological Institute, Brain Imaging Centre, McGill University, Canada
| | - B Freeze
- Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA
| | - M Dadar
- Montreal Neurological Institute, Brain Imaging Centre, McGill University, Canada
| | - D L Collins
- Montreal Neurological Institute, Brain Imaging Centre, McGill University, Canada
| | - A Dagher
- Montreal Neurological Institute, Brain Imaging Centre, McGill University, Canada
| | - A Raj
- Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA; Department of Radiology, UCSF School of Medicine, San Francisco, CA, USA.
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205
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Zemla JC, Austerweil JL. Analyzing Knowledge Retrieval Impairments Associated with Alzheimer's Disease Using Network Analyses. COMPLEXITY 2019; 2019:4203158. [PMID: 31341377 PMCID: PMC6656530 DOI: 10.1155/2019/4203158] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
A defining characteristic of Alzheimer's disease is difficulty in retrieving semantic memories, or memories encoding facts and knowledge. While it has been suggested that this impairment is caused by a degradation of the semantic store, the precise ways in which the semantic store is degraded are not well understood. Using a longitudinal corpus of semantic fluency data (listing of items in a category), we derive semantic network representations of patients with Alzheimer's disease and of healthy controls. We contrast our network-based approach with analyzing fluency data with the standard method of counting the total number of items and perseverations in fluency data. We find that the networks of Alzheimer's patients are more connected and that those connections are more randomly distributed than the connections in networks of healthy individuals. These results suggest that the semantic memory impairment of Alzheimer's patients can be modeled through the inclusion of spurious associations between unrelated concepts in the semantic store. We also find that information from our network analysis of fluency data improves prediction of patient diagnosis compared to traditional measures of the semantic fluency task.
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Affiliation(s)
- Jeffrey C Zemla
- Department of Psychology, University of Wisconsin-Madison, USA
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206
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Li K, Luo X, Zeng Q, Huang P, Shen Z, Xu X, Xu J, Wang C, Zhou J, Zhang M. Gray matter structural covariance networks changes along the Alzheimer's disease continuum. Neuroimage Clin 2019; 23:101828. [PMID: 31029051 PMCID: PMC6484365 DOI: 10.1016/j.nicl.2019.101828] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2018] [Revised: 04/01/2019] [Accepted: 04/15/2019] [Indexed: 11/24/2022]
Abstract
Alzheimer's disease (AD) has a long neuropathological accumulation phase before the onset of dementia. Such AD neuropathological deposition between neurons impairs the synaptic communication, resulting in networks disorganization. Our study aimed to explore the evolution patterns of gray matter structural covariance networks (SCNs) along AD continuum. Based on the AT(N) (i.e., Amyloid/Tau/Neurodegeneration) pathological classification system, we classified subjects into four groups using cerebrospinal fluid amyloid-beta1-42 (A) and phosphorylated tau protein181 (T). We identified 101 subjects with normal AD biomarkers (A-T-), 40 subjects with Alzheimer's pathologic change (A + T-), 101 subjects with biological AD (A + T+) and 91 AD with dementia (demented subjects with A + T+). We used four regions of interest to anchor default mode network (DMN, medial temporal subsystem and midline core subsystem), salience network (SN) and executive control network (ECN). Finally, we used a multi-regression model-based linear-interaction analysis to assess the SCN changes. Along the disease progression, DMN and SN showed increased structural association at the early stage while decreased structural association at the late stage. Moreover, ECN showed progressively increased structural association as AD neuropathological profiles progress. In conclusion, this study found the dynamic trajectory of SCNs changes along the AD continuum and support the network disconnection hypothesis underlying AD neuropathological progression. Further, SCN may potentially serve as an effective AD biomarker.
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Affiliation(s)
- Kaicheng Li
- Department of Radiology, School of Medicine, 2nd Affiliated Hospital of Zhejiang University, China
| | - Xiao Luo
- Department of Radiology, School of Medicine, 2nd Affiliated Hospital of Zhejiang University, China
| | - Qingze Zeng
- Department of Radiology, School of Medicine, 2nd Affiliated Hospital of Zhejiang University, China
| | - Peiyu Huang
- Department of Radiology, School of Medicine, 2nd Affiliated Hospital of Zhejiang University, China
| | - Zhujing Shen
- Department of Radiology, School of Medicine, 2nd Affiliated Hospital of Zhejiang University, China
| | - Xiaojun Xu
- Department of Radiology, School of Medicine, 2nd Affiliated Hospital of Zhejiang University, China
| | - Jingjing Xu
- Department of Radiology, School of Medicine, 2nd Affiliated Hospital of Zhejiang University, China
| | - Chao Wang
- Department of Radiology, School of Medicine, 2nd Affiliated Hospital of Zhejiang University, China
| | - Jiong Zhou
- Department of Neurology, School of Medicine, 2nd Affiliated Hospital of Zhejiang University, China
| | - Minming Zhang
- Department of Radiology, School of Medicine, 2nd Affiliated Hospital of Zhejiang University, China.
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207
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Butler PM, Chiong W, Perry DC, Miller ZA, Gennatas ED, Brown JA, Pasquini L, Karydas A, Dokuru D, Coppola G, Sturm VE, Boxer AL, Gorno-Tempini ML, Rosen HJ, Kramer JH, Miller BL, Seeley WW. Dopamine receptor D 4 (DRD 4) polymorphisms with reduced functional potency intensify atrophy in syndrome-specific sites of frontotemporal dementia. Neuroimage Clin 2019; 23:101822. [PMID: 31003069 PMCID: PMC6475809 DOI: 10.1016/j.nicl.2019.101822] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2018] [Revised: 04/04/2019] [Accepted: 04/09/2019] [Indexed: 11/23/2022]
Abstract
OBJECTIVE We aimed to understand the impact of dopamine receptor D4 (DRD4) polymorphisms on neurodegeneration in patients with dementia. We hypothesized that DRD4dampened-variants with reduced functional potency would be associated with greater atrophy in regions with higher receptor density. Given that DRD4 is concentrated in anterior regions of the limbic and cortical forebrain we anticipated genotype effects in patients with a more rostral pattern of neurodegeneration. METHODS 337 subjects, including healthy controls, patients with Alzheimer's disease (AD) and frontotemporal dementia (FTD) underwent genotyping, structural MRI, and cognitive/behavioral testing. We conducted whole-brain voxel-based morphometry to examine the relationship between DRD4 genotypes and brain atrophy patterns within and across groups. General linear modeling was used to evaluate relationships between genotype and cognitive/behavioral measures. RESULTS DRD4 dampened-variants predicted gray matter atrophy in disease-specific regions of FTD in anterior cingulate, ventromedial prefrontal, orbitofrontal and insular cortices on the right greater than the left. Genotype predicted greater apathy and repetitive motor disturbance in patients with FTD. These results covaried with frontoinsular cortical atrophy. Peak atrophy patterned along regions of neuroanatomic vulnerability in FTD-spectrum disorders. In AD subjects and controls, genotype did not impact gray matter intensity. CONCLUSIONS We conclude that DRD4 polymorphisms with reduced functional potency exacerbate neuronal injury in sites of higher receptor density, which intersect with syndrome-specific regions undergoing neurodegeneration in FTD.
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Affiliation(s)
- P M Butler
- Department of Neurology, Memory and Aging Center, University of California San Francisco, San Francisco, CA, USA.
| | - W Chiong
- Department of Neurology, Memory and Aging Center, University of California San Francisco, San Francisco, CA, USA
| | - D C Perry
- Department of Neurology, Memory and Aging Center, University of California San Francisco, San Francisco, CA, USA
| | - Z A Miller
- Department of Neurology, Memory and Aging Center, University of California San Francisco, San Francisco, CA, USA
| | - E D Gennatas
- Department of Neurology, Memory and Aging Center, University of California San Francisco, San Francisco, CA, USA
| | - J A Brown
- Department of Neurology, Memory and Aging Center, University of California San Francisco, San Francisco, CA, USA
| | - L Pasquini
- Department of Neurology, Memory and Aging Center, University of California San Francisco, San Francisco, CA, USA
| | - A Karydas
- Department of Neurology, Memory and Aging Center, University of California San Francisco, San Francisco, CA, USA
| | - D Dokuru
- Departments of Psychiatry and Neurology, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, USA
| | - G Coppola
- Departments of Psychiatry and Neurology, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, USA
| | - V E Sturm
- Department of Neurology, Memory and Aging Center, University of California San Francisco, San Francisco, CA, USA
| | - A L Boxer
- Department of Neurology, Memory and Aging Center, University of California San Francisco, San Francisco, CA, USA
| | - M L Gorno-Tempini
- Department of Neurology, Memory and Aging Center, University of California San Francisco, San Francisco, CA, USA
| | - H J Rosen
- Department of Neurology, Memory and Aging Center, University of California San Francisco, San Francisco, CA, USA
| | - J H Kramer
- Department of Neurology, Memory and Aging Center, University of California San Francisco, San Francisco, CA, USA
| | - B L Miller
- Department of Neurology, Memory and Aging Center, University of California San Francisco, San Francisco, CA, USA
| | - W W Seeley
- Department of Neurology, Memory and Aging Center, University of California San Francisco, San Francisco, CA, USA
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208
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Ranasinghe KG, Kothare H, Kort N, Hinkley LB, Beagle AJ, Mizuiri D, Honma SM, Lee R, Miller BL, Gorno-Tempini ML, Vossel KA, Houde JF, Nagarajan SS. Neural correlates of abnormal auditory feedback processing during speech production in Alzheimer's disease. Sci Rep 2019; 9:5686. [PMID: 30952883 PMCID: PMC6450891 DOI: 10.1038/s41598-019-41794-x] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2018] [Accepted: 03/13/2019] [Indexed: 11/24/2022] Open
Abstract
Accurate integration of sensory inputs and motor commands is essential to achieve successful behavioral goals. A robust model of sensorimotor integration is the pitch perturbation response, in which speakers respond rapidly to shifts of the pitch in their auditory feedback. In a previous study, we demonstrated abnormal sensorimotor integration in patients with Alzheimer's disease (AD) with an abnormally enhanced behavioral response to pitch perturbation. Here we examine the neural correlates of the abnormal pitch perturbation response in AD patients, using magnetoencephalographic imaging. The participants phonated the vowel /α/ while a real-time signal processor briefly perturbed the pitch (100 cents, 400 ms) of their auditory feedback. We examined the high-gamma band (65-150 Hz) responses during this task. AD patients showed significantly reduced left prefrontal activity during the early phase of perturbation and increased right middle temporal activity during the later phase of perturbation, compared to controls. Activity in these brain regions significantly correlated with the behavioral response. These results demonstrate that impaired prefrontal modulation of speech-motor-control network and additional recruitment of right temporal regions are significant mediators of aberrant sensorimotor integration in patients with AD. The abnormal neural integration mechanisms signify the contribution of cortical network dysfunction to cognitive and behavioral deficits in AD.
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Affiliation(s)
- Kamalini G Ranasinghe
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA, 94158, USA.
| | - Hardik Kothare
- Speech Neuroscience Laboratory, Department of Otolaryngology - Head and Neck Surgery, University of California San Francisco, San Francisco, CA, 94143, USA
- Biomagnetic Imaging Laboratory, Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, 94143, USA
- UC Berkeley - UCSF, Graduate Program in Bioengineering, San Francisco, CA, USA
| | - Naomi Kort
- Speech Neuroscience Laboratory, Department of Otolaryngology - Head and Neck Surgery, University of California San Francisco, San Francisco, CA, 94143, USA
- Biomagnetic Imaging Laboratory, Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, 94143, USA
| | - Leighton B Hinkley
- Speech Neuroscience Laboratory, Department of Otolaryngology - Head and Neck Surgery, University of California San Francisco, San Francisco, CA, 94143, USA
- Biomagnetic Imaging Laboratory, Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, 94143, USA
| | - Alexander J Beagle
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Danielle Mizuiri
- Biomagnetic Imaging Laboratory, Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, 94143, USA
| | - Susanne M Honma
- Biomagnetic Imaging Laboratory, Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, 94143, USA
| | - Richard Lee
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Bruce L Miller
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Maria Luisa Gorno-Tempini
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Keith A Vossel
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA, 94158, USA
- N. Bud Grossman Center for Memory Research and Care, Institute for Translational Neuroscience, and Department of Neurology, University of Minnesota, Minneapolis, MN, 55455, USA
| | - John F Houde
- Speech Neuroscience Laboratory, Department of Otolaryngology - Head and Neck Surgery, University of California San Francisco, San Francisco, CA, 94143, USA
| | - Srikantan S Nagarajan
- Speech Neuroscience Laboratory, Department of Otolaryngology - Head and Neck Surgery, University of California San Francisco, San Francisco, CA, 94143, USA
- Biomagnetic Imaging Laboratory, Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, 94143, USA
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209
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Sotiropoulos SN, Zalesky A. Building connectomes using diffusion MRI: why, how and but. NMR IN BIOMEDICINE 2019; 32:e3752. [PMID: 28654718 PMCID: PMC6491971 DOI: 10.1002/nbm.3752] [Citation(s) in RCA: 154] [Impact Index Per Article: 30.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2016] [Revised: 04/05/2017] [Accepted: 05/03/2017] [Indexed: 05/14/2023]
Abstract
Why has diffusion MRI become a principal modality for mapping connectomes in vivo? How do different image acquisition parameters, fiber tracking algorithms and other methodological choices affect connectome estimation? What are the main factors that dictate the success and failure of connectome reconstruction? These are some of the key questions that we aim to address in this review. We provide an overview of the key methods that can be used to estimate the nodes and edges of macroscale connectomes, and we discuss open problems and inherent limitations. We argue that diffusion MRI-based connectome mapping methods are still in their infancy and caution against blind application of deep white matter tractography due to the challenges inherent to connectome reconstruction. We review a number of studies that provide evidence of useful microstructural and network properties that can be extracted in various independent and biologically relevant contexts. Finally, we highlight some of the key deficiencies of current macroscale connectome mapping methodologies and motivate future developments.
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Affiliation(s)
- Stamatios N. Sotiropoulos
- Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUK
- Sir Peter Mansfield Imaging Centre, School of MedicineUniversity of NottinghamNottinghamUK
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre and Melbourne School of EngineeringUniversity of MelbourneVictoriaAustralia
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210
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Zeighami Y, Fereshtehnejad SM, Dadar M, Collins DL, Postuma RB, Mišić B, Dagher A. A clinical-anatomical signature of Parkinson's disease identified with partial least squares and magnetic resonance imaging. Neuroimage 2019; 190:69-78. [DOI: 10.1016/j.neuroimage.2017.12.050] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2017] [Revised: 12/14/2017] [Accepted: 12/15/2017] [Indexed: 12/11/2022] Open
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211
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Abstract
Most common neurodegenerative diseases feature deposition of protein amyloids and degeneration of brain networks. Amyloids are ordered protein assemblies that can act as templates for their own replication through monomer addition. Evidence suggests that this characteristic may underlie the progression of pathology in neurodegenerative diseases. Many different amyloid proteins, including Aβ, tau, and α-synuclein, exhibit properties similar to those of infectious prion protein in experimental systems: discrete and self-replicating amyloid structures, transcellular propagation of aggregation, and transmissible neuropathology. This review discusses the contribution of prion phenomena and transcellular propagation to the progression of pathology in common neurodegenerative diseases such as Alzheimer's and Parkinson's. It reviews fundamental events such as cell entry, amplification, and transcellular movement. It also discusses amyloid strains, which produce distinct patterns of neuropathology and spread through the nervous system. These concepts may impact the development of new diagnostic and therapeutic strategies.
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Affiliation(s)
- Jaime Vaquer-Alicea
- Center for Alzheimer's and Neurodegenerative Diseases, University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA;
| | - Marc I Diamond
- Center for Alzheimer's and Neurodegenerative Diseases, University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA;
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212
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Bakshi S, Chelliah V, Chen C, van der Graaf PH. Mathematical Biology Models of Parkinson's Disease. CPT Pharmacometrics Syst Pharmacol 2019; 8:77-86. [PMID: 30358157 PMCID: PMC6389348 DOI: 10.1002/psp4.12362] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2018] [Accepted: 09/19/2018] [Indexed: 12/27/2022] Open
Abstract
Parkinsons disease (PD) is a progressive neurodegenerative disease with substantial and growing socio-economic burden. In this multifactorial disease, aging, environmental, and genetic factors contribute to neurodegeneration and dopamine (DA) deficiency in the brain. Treatments aimed at DA restoration provide symptomatic relief, however, no disease modifying treatments are available, and PD remains incurable to date. Mathematical modeling can help understand such complex multifactorial neurological diseases. We review mathematical modeling efforts in PD with a focus on mechanistic models of pathogenic processes. We consider models of α-synuclein (Asyn) aggregation, feedbacks among Asyn, DA, and mitochondria and proteolytic systems, as well as pathology propagation through the brain. We hope that critical understanding of existing literature will pave the way to the development of quantitative systems pharmacology models to aid PD drug discovery and development.
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Affiliation(s)
- Suruchi Bakshi
- Certara QSPBredaThe Netherlands
- Systems Biomedicine and PharmacologyLeiden Academic Centre for Drug Research (LACDR)Leiden UniversityLeidenThe Netherlands
| | | | - Chao Chen
- Clinical Pharmacology Modelling & SimulationGlaxoSmithKlineUxbridgeUK
| | - Piet H. van der Graaf
- Systems Biomedicine and PharmacologyLeiden Academic Centre for Drug Research (LACDR)Leiden UniversityLeidenThe Netherlands
- Certara QSPCanterbury
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213
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Scherr M, Utz L, Tahmasian M, Pasquini L, Grothe MJ, Rauschecker JP, Grimmer T, Drzezga A, Sorg C, Riedl V. Effective connectivity in the default mode network is distinctively disrupted in Alzheimer's disease-A simultaneous resting-state FDG-PET/fMRI study. Hum Brain Mapp 2019; 42:4134-4143. [PMID: 30697878 DOI: 10.1002/hbm.24517] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2018] [Revised: 12/08/2018] [Accepted: 12/28/2018] [Indexed: 02/02/2023] Open
Abstract
A prominent finding of postmortem and molecular imaging studies on Alzheimer's disease (AD) is the accumulation of neuropathological proteins in brain regions of the default mode network (DMN). Molecular models suggest that the progression of disease proteins depends on the directionality of signaling pathways. At network level, effective connectivity (EC) reflects directionality of signaling pathways. We hypothesized a specific pattern of EC in the DMN of patients with AD, related to cognitive impairment. Metabolic connectivity mapping is a novel measure of EC identifying regions of signaling input based on neuroenergetics. We simultaneously acquired resting-state functional MRI and FDG-PET data from patients with early AD (n = 35) and healthy subjects (n = 18) on an integrated PET/MR scanner. We identified two distinct subnetworks of EC in the DMN of healthy subjects: an anterior part with bidirectional EC between hippocampus and medial prefrontal cortex and a posterior part with predominant input into medial parietal cortex. Patients had reduced input into the medial parietal system and absent input from hippocampus into medial prefrontal cortex (p < 0.05, corrected). In a multiple linear regression with unimodal imaging and EC measures (F4,25 = 5.63, p = 0.002, r2 = 0.47), we found that EC (β = 0.45, p = 0.012) was stronger associated with cognitive deficits in patients than any of the PET and fMRI measures alone. Our approach indicates specific disruptions of EC in the DMN of patients with AD and might be suitable to test molecular theories about downstream and upstream spreading of neuropathology in AD.
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Affiliation(s)
- Martin Scherr
- Department of Psychiatry and Psychotherapy, Technische Universität München (TUM), München, Germany.,TUM-Neuroimaging Center (TUM-NIC), Klinikum Rechts der Isar, München, Germany.,Department of Neurology, Christian Doppler Medical Centre, Paracelsus Medical University Salzburg and Centre for Cognitive Neurosciences, Salzburg, Austria
| | - Lukas Utz
- TUM-Neuroimaging Center (TUM-NIC), Klinikum Rechts der Isar, München, Germany.,Department of Neuroradiology, Technische Universität München (TUM), München, Germany.,Institute for Advanced Study, Technische Universität München (TUM), München, Germany
| | - Masoud Tahmasian
- Institute of Medical Science and Technology, Shahid Beheshti University, Tehran, Iran
| | - Lorenzo Pasquini
- TUM-Neuroimaging Center (TUM-NIC), Klinikum Rechts der Isar, München, Germany.,Department of Neuroradiology, Technische Universität München (TUM), München, Germany.,Memory and Aging Center, Department of Neurology, University of California, San Francisco, California
| | - Michel J Grothe
- Department for Clinical Research, German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany
| | - Josef P Rauschecker
- Institute for Advanced Study, Technische Universität München (TUM), München, Germany.,Laboratory of Integrative Neuroscience and Cognition, Georgetown University Medical Center, Washington, District of Columbia
| | - Timo Grimmer
- Department of Psychiatry and Psychotherapy, Technische Universität München (TUM), München, Germany.,TUM-Neuroimaging Center (TUM-NIC), Klinikum Rechts der Isar, München, Germany
| | | | - Christian Sorg
- Department of Psychiatry and Psychotherapy, Technische Universität München (TUM), München, Germany.,TUM-Neuroimaging Center (TUM-NIC), Klinikum Rechts der Isar, München, Germany.,Department of Neuroradiology, Technische Universität München (TUM), München, Germany
| | - Valentin Riedl
- TUM-Neuroimaging Center (TUM-NIC), Klinikum Rechts der Isar, München, Germany.,Department of Neuroradiology, Technische Universität München (TUM), München, Germany.,Department of Nuclear Medicine, Technische Universität München (TUM), München, Germany
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214
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Raj A, Iturria-Medina Y. Editorial: Network Spread Models of Neurodegenerative Diseases. Front Neurol 2019; 9:1159. [PMID: 30671020 PMCID: PMC6331437 DOI: 10.3389/fneur.2018.01159] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2018] [Accepted: 12/14/2018] [Indexed: 12/22/2022] Open
Affiliation(s)
- Ashish Raj
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States.,Department of Radiology, University of California at San Francisco, San Francisco, CA, United States
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215
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Cauda F, Nani A, Manuello J, Liloia D, Tatu K, Vercelli U, Duca S, Fox PT, Costa T. The alteration landscape of the cerebral cortex. Neuroimage 2019; 184:359-371. [PMID: 30237032 PMCID: PMC7384593 DOI: 10.1016/j.neuroimage.2018.09.036] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2018] [Revised: 08/24/2018] [Accepted: 09/14/2018] [Indexed: 01/12/2023] Open
Abstract
Growing evidence is challenging the assumption that brain disorders are diagnostically clear-cut categories. Transdiagnostic studies show that a set of cerebral areas is frequently altered in a variety of psychiatric as well as neurological syndromes. In order to provide a map of the altered areas in the pathological brain we devised a metric, called alteration entropy (A-entropy), capable of denoting the "structural alteration variety" of an altered region. Using the whole voxel-based morphometry database of BrainMap, we were able to differentiate the brain areas exhibiting a high degree of overlap between different neuropathologies (or high value of A-entropy) from those exhibiting a low degree of overlap (or low value of A-entropy). The former, which are parts of large-scale brain networks with attentional, emotional, salience, and premotor functions, are thought to be more vulnerable to a great range of brain diseases; while the latter, which include the sensorimotor, visual, inferior temporal, and supramarginal regions, are thought to be more informative about the specific impact of brain diseases. Since low A-entropy areas appear to be altered by a smaller number of brain disorders, they are more informative than the areas characterized by high values of A-entropy. It is also noteworthy that even the areas showing low values of A-entropy are substantially altered by a variety of brain disorders. In fact, no cerebral area appears to be only altered by a specific disorder. Our study shows that the overlap of areas with high A-entropy provides support for a transdiagnostic approach to brain disorders but, at the same time, suggests that fruitful differences can be traced among brain diseases, as some areas can exhibit an alteration profile more specific to certain disorders than to others.
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Affiliation(s)
- Franco Cauda
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Department of Psychology, University of Turin, Turin, Italy; FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy.
| | - Andrea Nani
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Department of Psychology, University of Turin, Turin, Italy; FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy
| | - Jordi Manuello
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Department of Psychology, University of Turin, Turin, Italy; FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy
| | - Donato Liloia
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Department of Psychology, University of Turin, Turin, Italy; FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy
| | - Karina Tatu
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Department of Psychology, University of Turin, Turin, Italy; FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy
| | - Ugo Vercelli
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Department of Psychology, University of Turin, Turin, Italy; FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy
| | - Sergio Duca
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy
| | - Peter T Fox
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, USA; South Texas Veterans Health Care System, USA
| | - Tommaso Costa
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Department of Psychology, University of Turin, Turin, Italy; FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy
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216
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Darby RR, Joutsa J, Fox MD. Network localization of heterogeneous neuroimaging findings. Brain 2019; 142:70-79. [PMID: 30551186 PMCID: PMC6308311 DOI: 10.1093/brain/awy292] [Citation(s) in RCA: 85] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2018] [Revised: 09/06/2018] [Accepted: 10/02/2018] [Indexed: 01/23/2023] Open
Abstract
Studies of the same disease often implicate different brain regions, contributing to a perceived reproducibility crisis in neuroimaging. Here, we leverage the normative human brain connectome to test whether seemingly heterogeneous neuroimaging findings localize to connected brain networks. We use neurodegenerative disease, and specifically Alzheimer's disease, as our example as it is one of the diseases that has been studied the most using neuroimaging. First, we show that neuroimaging findings in Alzheimer's disease occur in different brain regions across different studies but localize to the same functionally connected brain network. Second, we show that neuroimaging findings across different neurodegenerative diseases (Alzheimer's disease, frontotemporal dementia, corticobasal syndrome, and progressive non-fluent aphasia) localize to different disease-specific brain networks. Finally, we show that neuroimaging findings for a specific symptom within a disease (delusions in Alzheimer's disease) localize to a symptom-specific brain network. Our results suggest that neuroimaging studies that appear poorly reproducible may identify different regions within the same connected brain network. Human connectome data can be used to link heterogeneous neuroimaging findings to common neuroanatomy, improving localization of neuropsychiatric diseases and symptoms.
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Affiliation(s)
- R Ryan Darby
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
- Berenson-Allen Center for Noninvasive Brain Stimulation, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical Center, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Juho Joutsa
- Berenson-Allen Center for Noninvasive Brain Stimulation, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical Center, Boston, MA, USA
- Athinoula A. Martinos Centre for Biomedical Imaging, Massachusett General Hospital, Harvard Medical School, Charlestown, MA, USA
- Department of Neurology, University of Turku, Turku, Finland
- Division of Clinical Neurosciences, Turku University Hospital, Turku, Finland
| | - Michael D Fox
- Berenson-Allen Center for Noninvasive Brain Stimulation, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical Center, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Athinoula A. Martinos Centre for Biomedical Imaging, Massachusett General Hospital, Harvard Medical School, Charlestown, MA, USA
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217
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Hermanowicz-Sobieraj B, Bogus-Nowakowska K, Równiak M, Robak A. Ontogeny of calcium-binding proteins in the cingulate cortex of the guinea pig: The same onset but different developmental patterns. Ann Anat 2018; 222:103-113. [PMID: 30566895 DOI: 10.1016/j.aanat.2018.11.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2018] [Revised: 11/27/2018] [Accepted: 11/29/2018] [Indexed: 10/27/2022]
Abstract
This paper compared the density of calbindin D28k (CB), calretinin (CR) and parvalbumin (PV) containing neurons in prenatal, newborn and postnatal periods in the cingulate cortex (CC) of the guinea pig as an animal model. The distribution and co-distribution among calcium-binding proteins (CaBPs) was also investigated during the entire ontogeny. The study found that CB-positive neurons exhibited the highest density in the developing CC. The CC development in the prenatal period took place with a high level of CB and CR immunoreactivity and both of these proteins reached peak density during fetal life. The density of PV-positive neurons, in contrast to CB and CR-positive neurons, reached high levels postnatally. The observed changes of the CaBPs-positive neuron density in the developing CC coincide with developmental events in the guinea pig. E.g. the eyes opening moment may be preceded by elevated levels of CB and CR at E50, whereas high immunoreactivity of PV from P10 to P40 with a peak at P20 may indicate the participation of PV in enhancement of the inhibitory cortical pathway maturation.
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Affiliation(s)
- Beata Hermanowicz-Sobieraj
- Department of Animal Anatomy and Physiology, Faculty of Biology and Biotechnology, University of Warmia and Mazury in Olsztyn, Pl. Łódzki 3, 10-727 Olsztyn, Poland.
| | - Krystyna Bogus-Nowakowska
- Department of Animal Anatomy and Physiology, Faculty of Biology and Biotechnology, University of Warmia and Mazury in Olsztyn, Pl. Łódzki 3, 10-727 Olsztyn, Poland
| | - Maciej Równiak
- Department of Animal Anatomy and Physiology, Faculty of Biology and Biotechnology, University of Warmia and Mazury in Olsztyn, Pl. Łódzki 3, 10-727 Olsztyn, Poland
| | - Anna Robak
- Department of Animal Anatomy and Physiology, Faculty of Biology and Biotechnology, University of Warmia and Mazury in Olsztyn, Pl. Łódzki 3, 10-727 Olsztyn, Poland.
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218
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Wang J, Khosrowabadi R, Ng KK, Hong Z, Chong JSX, Wang Y, Chen CY, Hilal S, Venketasubramanian N, Wong TY, Chen CLH, Ikram MK, Zhou J. Alterations in Brain Network Topology and Structural-Functional Connectome Coupling Relate to Cognitive Impairment. Front Aging Neurosci 2018; 10:404. [PMID: 30618711 PMCID: PMC6300727 DOI: 10.3389/fnagi.2018.00404] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2018] [Accepted: 11/23/2018] [Indexed: 12/13/2022] Open
Abstract
According to the network-based neurodegeneration hypothesis, neurodegenerative diseases target specific large-scale neural networks, such as the default mode network, and may propagate along the structural and functional connections within and between these brain networks. Cognitive impairment no dementia (CIND) represents an early prodromal stage but few studies have examined brain topological changes within and between brain structural and functional networks. To this end, we studied the structural networks [diffusion magnetic resonance imaging (MRI)] and functional networks (task-free functional MRI) in CIND (61 mild, 56 moderate) and healthy older adults (97 controls). Structurally, compared with controls, moderate CIND had lower global efficiency, and lower nodal centrality and nodal efficiency in the thalamus, somatomotor network, and higher-order cognitive networks. Mild CIND only had higher nodal degree centrality in dorsal parietal regions. Functional differences were more subtle, with both CIND groups showing lower nodal centrality and efficiency in temporal and somatomotor regions. Importantly, CIND generally had higher structural-functional connectome correlation than controls. The higher structural-functional topological similarity was undesirable as higher correlation was associated with poorer verbal memory, executive function, and visuoconstruction. Our findings highlighted the distinct and progressive changes in brain structural-functional networks at the prodromal stage of neurodegenerative diseases.
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Affiliation(s)
- Juan Wang
- Center for Cognitive Neuroscience, Neuroscience and Behavioral Disorders Program, Duke-National University of Singapore Medical School, Singapore, Singapore
| | - Reza Khosrowabadi
- Center for Cognitive Neuroscience, Neuroscience and Behavioral Disorders Program, Duke-National University of Singapore Medical School, Singapore, Singapore.,Institute for Cognitive and Brain Sciences, Shahid Beheshti University, Tehran, Iran
| | - Kwun Kei Ng
- Center for Cognitive Neuroscience, Neuroscience and Behavioral Disorders Program, Duke-National University of Singapore Medical School, Singapore, Singapore
| | - Zhaoping Hong
- Center for Cognitive Neuroscience, Neuroscience and Behavioral Disorders Program, Duke-National University of Singapore Medical School, Singapore, Singapore
| | - Joanna Su Xian Chong
- Center for Cognitive Neuroscience, Neuroscience and Behavioral Disorders Program, Duke-National University of Singapore Medical School, Singapore, Singapore
| | - Yijun Wang
- Center for Cognitive Neuroscience, Neuroscience and Behavioral Disorders Program, Duke-National University of Singapore Medical School, Singapore, Singapore
| | - Chun-Yin Chen
- Center for Cognitive Neuroscience, Neuroscience and Behavioral Disorders Program, Duke-National University of Singapore Medical School, Singapore, Singapore
| | - Saima Hilal
- Department of Pharmacology, National University of Singapore, Singapore, Singapore
| | | | - Tien Yin Wong
- Memory Aging & Cognition Centre, National University Health System, Singapore, Singapore.,Singapore National Eye Centre, Singapore Eye Research Institute, Singapore, Singapore
| | | | - Mohammad Kamran Ikram
- Department of Pharmacology, National University of Singapore, Singapore, Singapore.,Memory Aging & Cognition Centre, National University Health System, Singapore, Singapore.,Singapore National Eye Centre, Singapore Eye Research Institute, Singapore, Singapore.,Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, Netherlands
| | - Juan Zhou
- Center for Cognitive Neuroscience, Neuroscience and Behavioral Disorders Program, Duke-National University of Singapore Medical School, Singapore, Singapore.,Clinical Imaging Research Centre, The Agency for Science, Technology and Research-National University of Singapore, Singapore, Singapore
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219
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Alderson TH, Bokde ALW, Kelso JAS, Maguire L, Coyle D. Metastable neural dynamics in Alzheimer's disease are disrupted by lesions to the structural connectome. Neuroimage 2018; 183:438-455. [PMID: 30130642 PMCID: PMC6374703 DOI: 10.1016/j.neuroimage.2018.08.033] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2018] [Revised: 07/22/2018] [Accepted: 08/15/2018] [Indexed: 12/16/2022] Open
Abstract
Current theory suggests brain regions interact to reconcile the competing demands of integration and segregation by leveraging metastable dynamics. An emerging consensus recognises the importance of metastability in healthy neural dynamics where the transition between network states over time is dependent upon the structural connectivity between brain regions. In Alzheimer's disease (AD) - the most common form of dementia - these couplings are progressively weakened, metastability of neural dynamics are reduced and cognitive ability is impaired. Accordingly, we use a joint empirical and computational approach to reveal how behaviourally relevant changes in neural metastability are contingent on the structural integrity of the anatomical connectome. We estimate the metastability of fMRI BOLD signal in subjects from across the AD spectrum and in healthy controls and demonstrate the dissociable effects of structural disconnection on synchrony versus metastability. In addition, we reveal the critical role of metastability in general cognition by demonstrating the link between an individuals cognitive performance and their metastable neural dynamic. Finally, using whole-brain computer modelling, we demonstrate how a healthy neural dynamic is conditioned upon the topological integrity of the structural connectome. Overall, the results of our joint computational and empirical analysis suggest an important causal relationship between metastable neural dynamics, cognition, and the structural efficiency of the anatomical connectome.
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Affiliation(s)
| | - Arun L W Bokde
- Trinity College Institute of Neuroscience and Cognitive Systems Group, Discipline of Psychiatry, School of Medicine, Trinity College Dublin, Ireland
| | - J A Scott Kelso
- Intelligent Systems Research Centre, Ulster University, UK; Center for Complex Systems and Brain Sciences, Florida Atlantic University, Boca Raton, USA
| | - Liam Maguire
- Intelligent Systems Research Centre, Ulster University, UK
| | - Damien Coyle
- Intelligent Systems Research Centre, Ulster University, UK
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220
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Hofmann JW, Seeley WW, Huang EJ. RNA Binding Proteins and the Pathogenesis of Frontotemporal Lobar Degeneration. ANNUAL REVIEW OF PATHOLOGY-MECHANISMS OF DISEASE 2018; 14:469-495. [PMID: 30355151 DOI: 10.1146/annurev-pathmechdis-012418-012955] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Frontotemporal dementia is a group of early onset dementia syndromes linked to underlying frontotemporal lobar degeneration (FTLD) pathology that can be classified based on the formation of abnormal protein aggregates involving tau and two RNA binding proteins, TDP-43 and FUS. Although elucidation of the mechanisms leading to FTLD pathology is in progress, recent advances in genetics and neuropathology indicate that a majority of FTLD cases with proteinopathy involving RNA binding proteins show highly congruent genotype-phenotype correlations. Specifically, recent studies have uncovered the unique properties of the low-complexity domains in RNA binding proteins that can facilitate liquid-liquid phase separation in the formation of membraneless organelles. Furthermore, there is compelling evidence that mutations in FTLD genes lead to dysfunction in diverse cellular pathways that converge on the endolysosomal pathway, autophagy, and neuroinflammation. Together, these results provide key mechanistic insights into the pathogenesis and potential therapeutic targets of FTLD.
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Affiliation(s)
- Jeffrey W Hofmann
- Department of Pathology, University of California, San Francisco, California 94143, USA;
| | - William W Seeley
- Department of Pathology, University of California, San Francisco, California 94143, USA; .,Department of Neurology, University of California, San Francisco, California 94148, USA
| | - Eric J Huang
- Department of Pathology, University of California, San Francisco, California 94143, USA; .,Pathology Service 113B, Veterans Affairs Medical Center, San Francisco, California 94121, USA
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221
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Kim WH, Racine AM, Adluru N, Hwang SJ, Blennow K, Zetterberg H, Carlsson CM, Asthana S, Koscik RL, Johnson SC, Bendlin BB, Singh V. Cerebrospinal fluid biomarkers of neurofibrillary tangles and synaptic dysfunction are associated with longitudinal decline in white matter connectivity: A multi-resolution graph analysis. Neuroimage Clin 2018; 21:101586. [PMID: 30502079 PMCID: PMC6411581 DOI: 10.1016/j.nicl.2018.10.024] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2018] [Revised: 10/10/2018] [Accepted: 10/21/2018] [Indexed: 11/24/2022]
Abstract
In addition to the development of beta amyloid plaques and neurofibrillary tangles, Alzheimer's disease (AD) involves the loss of connecting structures including degeneration of myelinated axons and synaptic connections. However, the extent to which white matter tracts change longitudinally, particularly in the asymptomatic, preclinical stage of AD, remains poorly characterized. In this study we used a novel graph wavelet algorithm to determine the extent to which microstructural brain changes evolve in concert with the development of AD neuropathology as observed using CSF biomarkers. A total of 118 participants with at least two diffusion tensor imaging (DTI) scans and one lumbar puncture for CSF were selected from two observational and longitudinally followed cohorts. CSF was assayed for pathology specific to AD (Aβ42 and phosphorylated-tau), neurodegeneration (total-tau), axonal degeneration (neurofilament light chain protein; NFL), and synaptic degeneration (neurogranin). Tractography was performed on DTI scans to obtain structural connectivity networks with 160 nodes where the nodes correspond to specific brain regions of interest (ROIs) and their connections were defined by DTI metrics (i.e., fractional anisotropy (FA) and mean diffusivity (MD)). For the analysis, we adopted a multi-resolution graph wavelet technique called Wavelet Connectivity Signature (WaCS) which derives higher order representations from DTI metrics at each brain connection. Our statistical analysis showed interactions between the CSF measures and the MRI time interval, such that elevated CSF biomarkers and longer time were associated with greater longitudinal changes in white matter microstructure (decreasing FA and increasing MD). Specifically, we detected a total of 17 fiber tracts whose WaCS representations showed an association between longitudinal decline in white matter microstructure and both CSF p-tau and neurogranin. While development of neurofibrillary tangles and synaptic degeneration are cortical phenomena, the results show that they are also associated with degeneration of underlying white matter tracts, a process which may eventually play a role in the development of cognitive decline and dementia.
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Affiliation(s)
- Won Hwa Kim
- Department of Computer Sciences and Engineering, University of Texas, Arlington, TX, U.S.A.; Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.
| | - Annie M Racine
- Institute for Aging Research, Harvard Medical School, Boston, MA, U.S.A.; Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Nagesh Adluru
- Waisman Laboratory for Brain Imaging and Behavior, University of Wisconsin - Madison, Madison, WI, USA
| | - Seong Jae Hwang
- Department of Computer Science, University of Wisconsin - Madison, Madison, WI, USA
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden; Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden; Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden; Institute of Neurology, University College London, London, UK; UK Dementia Research Institute at UCL, London, UK
| | - Cynthia M Carlsson
- Geriatric Research Education and Clinical Center, Wm. S. Middleton Veterans Hospital, Madison, WI, USA; Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Sanjay Asthana
- Geriatric Research Education and Clinical Center, Wm. S. Middleton Veterans Hospital, Madison, WI, USA; Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA; Wisconsin Alzheimer's Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Rebecca L Koscik
- Wisconsin Alzheimer's Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Sterling C Johnson
- Institute on Aging, University of Wisconsin - Madison, Madison, WI, USA; Geriatric Research Education and Clinical Center, Wm. S. Middleton Veterans Hospital, Madison, WI, USA; Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA; Wisconsin Alzheimer's Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA; Department of Psychiatry, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Barbara B Bendlin
- Geriatric Research Education and Clinical Center, Wm. S. Middleton Veterans Hospital, Madison, WI, USA; Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA; Wisconsin Alzheimer's Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Vikas Singh
- Department of Biostatistics and Medical Informatics, University of Wisconsin - Madison, Madison, WI, USA; Department of Computer Science, University of Wisconsin - Madison, Madison, WI, USA
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222
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Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference. Nat Commun 2018; 9:4273. [PMID: 30323170 PMCID: PMC6189176 DOI: 10.1038/s41467-018-05892-0] [Citation(s) in RCA: 236] [Impact Index Per Article: 39.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2017] [Accepted: 07/20/2018] [Indexed: 12/13/2022] Open
Abstract
The heterogeneity of neurodegenerative diseases is a key confound to disease understanding and treatment development, as study cohorts typically include multiple phenotypes on distinct disease trajectories. Here we introduce a machine-learning technique—Subtype and Stage Inference (SuStaIn)—able to uncover data-driven disease phenotypes with distinct temporal progression patterns, from widely available cross-sectional patient studies. Results from imaging studies in two neurodegenerative diseases reveal subgroups and their distinct trajectories of regional neurodegeneration. In genetic frontotemporal dementia, SuStaIn identifies genotypes from imaging alone, validating its ability to identify subtypes; further the technique reveals within-genotype heterogeneity. In Alzheimer’s disease, SuStaIn uncovers three subtypes, uniquely characterising their temporal complexity. SuStaIn provides fine-grained patient stratification, which substantially enhances the ability to predict conversion between diagnostic categories over standard models that ignore subtype (p = 7.18 × 10−4) or temporal stage (p = 3.96 × 10−5). SuStaIn offers new promise for enabling disease subtype discovery and precision medicine. Progressive diseases tend to be heterogeneous in their underlying aetiology mechanism, disease manifestation, and disease time course. Here, Young and colleagues devise a computational method to account for both phenotypic heterogeneity and temporal heterogeneity, and demonstrate it using two neurodegenerative disease cohorts.
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223
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Veitch DP, Weiner MW, Aisen PS, Beckett LA, Cairns NJ, Green RC, Harvey D, Jack CR, Jagust W, Morris JC, Petersen RC, Saykin AJ, Shaw LM, Toga AW, Trojanowski JQ. Understanding disease progression and improving Alzheimer's disease clinical trials: Recent highlights from the Alzheimer's Disease Neuroimaging Initiative. Alzheimers Dement 2018; 15:106-152. [PMID: 30321505 DOI: 10.1016/j.jalz.2018.08.005] [Citation(s) in RCA: 231] [Impact Index Per Article: 38.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2018] [Accepted: 08/21/2018] [Indexed: 02/06/2023]
Abstract
INTRODUCTION The overall goal of the Alzheimer's Disease Neuroimaging Initiative (ADNI) is to validate biomarkers for Alzheimer's disease (AD) clinical trials. ADNI is a multisite, longitudinal, observational study that has collected many biomarkers since 2004. Recent publications highlight the multifactorial nature of late-onset AD. We discuss selected topics that provide insights into AD progression and outline how this knowledge may improve clinical trials. METHODS We used standard methods to identify nearly 600 publications using ADNI data from 2016 and 2017 (listed in Supplementary Material and searchable at http://adni.loni.usc.edu/news-publications/publications/). RESULTS (1) Data-driven AD progression models supported multifactorial interactions rather than a linear cascade of events. (2) β-Amyloid (Aβ) deposition occurred concurrently with functional connectivity changes within the default mode network in preclinical subjects and was followed by specific and progressive disconnection of functional and anatomical networks. (3) Changes in functional connectivity, volumetric measures, regional hypometabolism, and cognition were detectable at subthreshold levels of Aβ deposition. 4. Tau positron emission tomography imaging studies detailed a specific temporal and spatial pattern of tau pathology dependent on prior Aβ deposition, and related to subsequent cognitive decline. 5. Clustering studies using a wide range of modalities consistently identified a "typical AD" subgroup and a second subgroup characterized by executive impairment and widespread cortical atrophy in preclinical and prodromal subjects. 6. Vascular pathology burden may act through both Aβ dependent and independent mechanisms to exacerbate AD progression. 7. The APOE ε4 allele interacted with cerebrovascular disease to impede Aβ clearance mechanisms. 8. Genetic approaches identified novel genetic risk factors involving a wide range of processes, and demonstrated shared genetic risk for AD and vascular disorders, as well as the temporal and regional pathological associations of established AD risk alleles. 9. Knowledge of early pathological changes guided the development of novel prognostic biomarkers for preclinical subjects. 10. Placebo populations of randomized controlled clinical trials had highly variable trajectories of cognitive change, underscoring the importance of subject selection and monitoring. 11. Selection criteria based on Aβ positivity, hippocampal volume, baseline cognitive/functional measures, and APOE ε4 status in combination with improved cognitive outcome measures were projected to decrease clinical trial duration and cost. 12. Multiple concurrent therapies targeting vascular health and other AD pathology in addition to Aβ may be more effective than single therapies. DISCUSSION ADNI publications from 2016 and 2017 supported the idea of AD as a multifactorial disease and provided insights into the complexities of AD disease progression. These findings guided the development of novel biomarkers and suggested that subject selection on the basis of multiple factors may lower AD clinical trial costs and duration. The use of multiple concurrent therapies in these trials may prove more effective in reversing AD disease progression.
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Affiliation(s)
- Dallas P Veitch
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA; Northern California Institute for Research and Education (NCIRE), Department of Veterans Affairs Medical Center, San Francisco, CA, USA
| | - Michael W Weiner
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA; Department of Radiology, University of California, San Francisco, CA, USA; Department of Medicine, University of California, San Francisco, CA, USA; Department of Psychiatry, University of California, San Francisco, CA, USA; Department of Neurology, University of California, San Francisco, CA, USA.
| | - Paul S Aisen
- Alzheimer's Therapeutic Research Institute, University of Southern California, San Diego, CA, USA
| | - Laurel A Beckett
- Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, CA, USA
| | - Nigel J Cairns
- Knight Alzheimer's Disease Research Center, Washington University School of Medicine, Saint Louis, MO, USA; Department of Neurology, Washington University School of Medicine, Saint Louis, MO, USA
| | - Robert C Green
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Danielle Harvey
- Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, CA, USA
| | | | - William Jagust
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA, USA
| | - John C Morris
- Knight Alzheimer's Disease Research Center, Washington University School of Medicine, Saint Louis, MO, USA
| | | | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Leslie M Shaw
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Arthur W Toga
- Laboratory of Neuroimaging, Institute of Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - John Q Trojanowski
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Institute on Aging, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Alzheimer's Disease Core Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Udall Parkinson's Research Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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Dyrba M, Grothe MJ, Mohammadi A, Binder H, Kirste T, Teipel SJ. Comparison of Different Hypotheses Regarding the Spread of Alzheimer’s Disease Using Markov Random Fields and Multimodal Imaging. J Alzheimers Dis 2018; 65:731-746. [DOI: 10.3233/jad-161197] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Martin Dyrba
- German Center for Neurodegenerative Diseases (DZNE), Site Rostock/Greifswald, Rostock, Germany
| | - Michel J. Grothe
- German Center for Neurodegenerative Diseases (DZNE), Site Rostock/Greifswald, Rostock, Germany
| | - Abdolreza Mohammadi
- Department of Methodology and Statistics, Tilburg University, Tilburg, The Netherlands
| | - Harald Binder
- Institute of Medical Biostatistics, Epidemiology and Informatics, University Medical Center, Johannes Gutenberg University, Mainz, Germany
| | - Thomas Kirste
- Mobile Multimedia Information Systems Group (MMIS), University of Rostock, Rostock, Germany
| | - Stefan J. Teipel
- German Center for Neurodegenerative Diseases (DZNE), Site Rostock/Greifswald, Rostock, Germany
- Clinic for Psychosomatic and Psychotherapeutic Medicine, University Medical Center Rostock, Rostock, Germany
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Grothe MJ, Sepulcre J, Gonzalez-Escamilla G, Jelistratova I, Schöll M, Hansson O, Teipel SJ. Molecular properties underlying regional vulnerability to Alzheimer's disease pathology. Brain 2018; 141:2755-2771. [PMID: 30016411 PMCID: PMC6113636 DOI: 10.1093/brain/awy189] [Citation(s) in RCA: 61] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2018] [Revised: 05/13/2018] [Accepted: 06/03/2018] [Indexed: 01/04/2023] Open
Abstract
Amyloid deposition and neurofibrillary degeneration in Alzheimer's disease specifically affect discrete neuronal systems, but the underlying mechanisms that render some brain regions more vulnerable to Alzheimer's disease pathology than others remain largely unknown. Here we studied molecular properties underlying these distinct regional vulnerabilities by analysing Alzheimer's disease-typical neuroimaging patterns of amyloid deposition and neurodegeneration in relation to regional gene expression profiles of the human brain. Graded patterns of brain-wide vulnerability to amyloid deposition and neurodegeneration in Alzheimer's disease were estimated by contrasting multimodal amyloid-sensitive PET and structural MRI data between patients with Alzheimer's disease dementia (n = 76) and healthy controls (n = 126) enrolled in the Alzheimer's Disease Neuroimaging Initiative (ADNI). Regional gene expression profiles were derived from brain-wide microarray measurements provided by the Allen brain atlas of the adult human brain transcriptome. In a hypothesis-driven analysis focusing on the genes coding for the amyloid precursor (APP) and tau proteins (MAPT), regional expression levels of APP were positively correlated with the severity of regional amyloid deposition (r = 0.44, P = 0.009), but not neurodegeneration (r = 0.01, P = 0.96), whereas the opposite pattern was observed for MAPT (neurodegeneration: r = 0.46, P = 0.006; amyloid: r = 0.08, P = 0.65). Using explorative gene set enrichment analysis, amyloid-vulnerable regions were found to be characterized by relatively low expression levels of gene sets implicated in protein synthesis and mitochondrial respiration. By contrast, neurodegeneration-vulnerable regions were characterized by relatively high expression levels of gene sets broadly implicated in neural plasticity, with biological functions ranging from neurite outgrowth and synaptic contact over intracellular signalling cascades to proteoglycan metabolism. At the individual gene level this data-driven analysis further corroborated the association between neurodegeneration and MAPT expression, and additionally identified associations with known tau kinases (CDK5, MAPK1/ERK2) alongside components of their intracellular (Ras-ERK) activation pathways. Sensitivity analyses showed that these pathology-specific imaging-genetic associations were largely robust against changes in some of the methodological parameters, including variation in the brain donor sample used for estimating regional gene expression profiles, and local variations in the Alzheimer's disease-typical imaging patterns when these were derived from an independent patient cohort (BioFINDER study). These findings highlight that the regionally selective vulnerability to Alzheimer's disease pathology relates to specific molecular-functional properties of the affected neural systems, and that the implicated biochemical pathways largely differ for amyloid accumulation versus neurodegeneration. The data provide novel insights into the complex pathophysiological mechanisms of Alzheimer's disease and point to pathology-specific treatment targets that warrant further exploration in independent studies.
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Affiliation(s)
- Michel J Grothe
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany
| | - Jorge Sepulcre
- Gordon Center for Medical Imaging, Division of Nuclear Medicine and Molecular Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
| | - Gabriel Gonzalez-Escamilla
- Section of Movement Disorders and Neurostimulation, Department of Neurology, Focus Program Translational Neuroscience (FTN), University Medical Center of the Johannes Gutenberg-University Mainz, Germany
| | | | - Michael Schöll
- Wallenberg Centre for Molecular and Translational Medicine and the Department of Psychiatry and Neurochemistry, University of Gothenburg, Sweden
- Clinical Memory Research Unit, Department of Clinical Sciences, Malmö, Lund University, Sweden
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences, Malmö, Lund University, Sweden
- Memory Clinic, Skåne University Hospital, Sweden
| | - Stefan J Teipel
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany
- Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany
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226
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Cognitive and structural cerebral changes in amnestic mild cognitive impairment due to Alzheimer's disease after multicomponent training. ALZHEIMERS & DEMENTIA-TRANSLATIONAL RESEARCH & CLINICAL INTERVENTIONS 2018; 4:473-480. [PMID: 30258976 PMCID: PMC6153377 DOI: 10.1016/j.trci.2018.02.003] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Introduction Information about how physical exercise affects patients with amnestic mild cognitive impairment (aMCI) due to Alzheimer's disease (AD) is still missing. This study evaluated the impact of multicomponent exercise training on cognition and brain structure in aMCI subjects with cerebral spinal fluid positive AD biomarkers. Methods Forty aMCI subjects were divided in training (multicomponent exercise thrice a week for 6 months) and nontraining groups. Assessments included cardiorespiratory fitness, neurocognitive tests, and a structural magnetic resonance imaging using 3.0 T scanner. FreeSurfer software analyzed hippocampal volume and cortical thickness. Results The training group showed increased volume in both hippocampi and better performance in episodic memory test after 6 months. In contrast, the nontraining group declined in functional activities, recognition, and cardiorespiratory fitness for the same period. Discussion Multicomponent exercise seems to improve hippocampal volume and episodic memory, and maintains VO2max in aMCI due to AD.
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Mandelli ML, Welch AE, Vilaplana E, Watson C, Battistella G, Brown JA, Possin KL, Hubbard HI, Miller ZA, Henry ML, Marx GA, Santos-Santos MA, Bajorek LP, Fortea J, Boxer A, Rabinovici G, Lee S, Deleon J, Rosen HJ, Miller BL, Seeley WW, Gorno-Tempini ML. Altered topology of the functional speech production network in non-fluent/agrammatic variant of PPA. Cortex 2018; 108:252-264. [PMID: 30292076 DOI: 10.1016/j.cortex.2018.08.002] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2017] [Revised: 03/07/2018] [Accepted: 08/02/2018] [Indexed: 12/13/2022]
Abstract
Non-fluent/agrammatic primary progressive aphasia (nfvPPA) is caused by neurodegeneration within the left fronto-insular speech and language production network (SPN). Graph theory is a branch of mathematics that studies network architecture (topology) by quantifying features based on its elements (nodes and connections). This approach has been recently applied to neuroimaging data to explore the complex architecture of the brain connectome, though few studies have exploited this technique in PPA. Here, we used graph theory on functional MRI resting state data from a group of 20 nfvPPA patients and 20 matched controls to investigate topological changes in response to focal neurodegeneration. We hypothesized that changes in the network architecture would be specific to the affected SPN in nfvPPA, while preserved in the spared default mode network (DMN). Topological configuration was quantified by hub location and global network metrics. Our findings showed a less efficiently wired and less optimally clustered SPN, while no changes were detected in the DMN. The SPN in the nfvPPA group showed a loss of hubs in the left fronto-parietal-temporal area and new critical nodes in the anterior left inferior-frontal and right frontal regions. Behaviorally, speech production score and rule violation errors correlated with the strength of functional connectivity of the left (lost) and right (new) regions respectively. This study shows that focal neurodegeneration within the SPN in nfvPPA is associated with network-specific topological alterations, with the loss and gain of crucial hubs and decreased global efficiency that were better accounted for through functional rather than structural changes. These findings support the hypothesis of selective network vulnerability in nfvPPA and may offer biomarkers for future behavioral intervention.
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Affiliation(s)
- Maria Luisa Mandelli
- Department of Neurology, Memory and Aging Center, University of California San Francisco, CA, USA.
| | - Ariane E Welch
- Department of Neurology, Memory and Aging Center, University of California San Francisco, CA, USA
| | - Eduard Vilaplana
- Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau - Biomedical Research Institute Sant Pau - Universitat Autonoma de Barcelona, Spain; Centro de Investigacion Biomedica en Red de Enfermedades Neurodegenerativas - CIBERNED, Spain
| | - Christa Watson
- Department of Neurology, Memory and Aging Center, University of California San Francisco, CA, USA
| | - Giovanni Battistella
- Department of Neurology, Memory and Aging Center, University of California San Francisco, CA, USA
| | - Jesse A Brown
- Department of Neurology, Memory and Aging Center, University of California San Francisco, CA, USA
| | - Katherine L Possin
- Department of Neurology, Memory and Aging Center, University of California San Francisco, CA, USA
| | - Honey I Hubbard
- Department of Communication Science and Disorders, Faculty of Rehabilitation Medicine, University of Alberta, Edmonton, AB, Canada
| | - Zachary A Miller
- Department of Neurology, Memory and Aging Center, University of California San Francisco, CA, USA
| | - Maya L Henry
- Department of Communication Sciences and Disorders, University of Texas, Austin, USA
| | - Gabe A Marx
- Department of Neurology, Memory and Aging Center, University of California San Francisco, CA, USA
| | - Miguel A Santos-Santos
- Cognition and Brain Plasticity Group [Bellvitge Biomedical Research Institute-IDIBELL], L'Hospitalet de Llobregat, Barcelona, Spain; Fundació ACE Memory Clinic and Research Center, Institut Catalá de Neurociències Aplicades, Barcelona, Spain
| | - Lynn P Bajorek
- Department of Neurology, Memory and Aging Center, University of California San Francisco, CA, USA
| | - Juan Fortea
- Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau - Biomedical Research Institute Sant Pau - Universitat Autonoma de Barcelona, Spain
| | - Adam Boxer
- Department of Neurology, Memory and Aging Center, University of California San Francisco, CA, USA
| | - Gil Rabinovici
- Department of Neurology, Memory and Aging Center, University of California San Francisco, CA, USA
| | - Suzee Lee
- Department of Neurology, Memory and Aging Center, University of California San Francisco, CA, USA
| | - Jessica Deleon
- Department of Neurology, Memory and Aging Center, University of California San Francisco, CA, USA
| | - Howard J Rosen
- Department of Neurology, Memory and Aging Center, University of California San Francisco, CA, USA
| | - Bruce L Miller
- Department of Neurology, Memory and Aging Center, University of California San Francisco, CA, USA
| | - William W Seeley
- Department of Neurology, Memory and Aging Center, University of California San Francisco, CA, USA; Department of Pathology, University of California San Francisco, CA, USA
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228
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Raj A, Powell F. Models of Network Spread and Network Degeneration in Brain Disorders. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2018; 3:788-797. [PMID: 30170711 DOI: 10.1016/j.bpsc.2018.07.012] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2017] [Revised: 07/11/2018] [Accepted: 07/11/2018] [Indexed: 01/01/2023]
Abstract
Network analysis can provide insight into key organizational principles of brain structure and help identify structural changes associated with brain disease. Though static differences between diseased and healthy networks are well characterized, the study of network dynamics, or how brain networks change over time, is increasingly central to understanding ongoing brain changes throughout disease. Accordingly, we present a short review of network models of spread, network dynamics, and network degeneration. Borrowing from recent suggestions, we divide this review into two processes by which brain networks can change: dynamics on networks, which are functional and pathological consequences taking place atop a static structural brain network; and dynamics of networks, which constitutes a changing structural brain network. We focus on diffusion magnetic resonance imaging-based structural or anatomic connectivity graphs. We address psychiatric disorders like schizophrenia; developmental disorders like epilepsy; stroke; and Alzheimer's disease and other neurodegenerative diseases.
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Affiliation(s)
- Ashish Raj
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California.
| | - Fon Powell
- Department of Radiology, Weill Cornell Medicine, New York, New York
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229
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Sami S, Williams N, Hughes LE, Cope TE, Rittman T, Coyle-Gilchrist ITS, Henson RN, Rowe JB. Neurophysiological signatures of Alzheimer's disease and frontotemporal lobar degeneration: pathology versus phenotype. Brain 2018; 141:2500-2510. [PMID: 30060017 PMCID: PMC6061803 DOI: 10.1093/brain/awy180] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2017] [Revised: 04/27/2018] [Accepted: 05/17/2018] [Indexed: 01/21/2023] Open
Abstract
The disruption of brain networks is characteristic of neurodegenerative dementias. However, it is controversial whether changes in connectivity reflect only the functional anatomy of disease, with selective vulnerability of brain networks, or the specific neurophysiological consequences of different neuropathologies within brain networks. We proposed that the oscillatory dynamics of cortical circuits reflect the tuning of local neural interactions, such that different pathologies are selective in their impact on the frequency spectrum of oscillations, whereas clinical syndromes reflect the anatomical distribution of pathology and physiological change. To test this hypothesis, we used magnetoencephalography from five patient groups, representing dissociated pathological subtypes and distributions across frontal, parietal and temporal lobes: amnestic Alzheimer's disease, posterior cortical atrophy, and three syndromes associated with frontotemporal lobar degeneration. We measured effective connectivity with graph theory-based measures of local efficiency, using partial directed coherence between sensors. As expected, each disease caused large-scale changes of neurophysiological brain networks, with reductions in local efficiency compared to controls. Critically however, the frequency range of altered connectivity was consistent across clinical syndromes that shared a likely underlying pathology, whilst the localization of changes differed between clinical syndromes. Multivariate pattern analysis of the frequency-specific topographies of local efficiency separated the disorders from each other and from controls (accuracy 62% to 100%, according to the groups' differences in likely pathology and clinical syndrome). The data indicate that magnetoencephalography has the potential to reveal specific changes in neurophysiology resulting from neurodegenerative disease. Our findings confirm that while clinical syndromes have characteristic anatomical patterns of abnormal connectivity that may be identified with other methods like structural brain imaging, the different mechanisms of neurodegeneration also cause characteristic spectral signatures of physiological coupling that are not accessible with structural imaging nor confounded by the neurovascular signalling of functional MRI. We suggest that these spectral characteristics of altered connectivity are the result of differential disruption of neuronal microstructure and synaptic physiology by Alzheimer's disease versus frontotemporal lobar degeneration.
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Affiliation(s)
- Saber Sami
- Department of Clinical Neurosciences, University of Cambridge, UK
| | | | - Laura E Hughes
- Department of Clinical Neurosciences, University of Cambridge, UK
- Medical Research Council Cognition and Brain Sciences Unit, Cambridge, UK
| | - Thomas E Cope
- Department of Clinical Neurosciences, University of Cambridge, UK
| | - Timothy Rittman
- Department of Clinical Neurosciences, University of Cambridge, UK
| | | | - Richard N Henson
- Medical Research Council Cognition and Brain Sciences Unit, Cambridge, UK
| | - James B Rowe
- Department of Clinical Neurosciences, University of Cambridge, UK
- Medical Research Council Cognition and Brain Sciences Unit, Cambridge, UK
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Dagher A, Zeighami Y. Testing the Protein Propagation Hypothesis of Parkinson Disease. J Exp Neurosci 2018; 12:1179069518786715. [PMID: 30013389 PMCID: PMC6043918 DOI: 10.1177/1179069518786715] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2018] [Accepted: 06/06/2018] [Indexed: 12/12/2022] Open
Abstract
One of the most exciting recent hypotheses in neurology is that most neurodegenerative diseases are caused by the neuron to neuron propagation of prion-like misfolded proteins. In Parkinson disease, the theory initially emerged from postmortem studies demonstrating a caudal-rostral progression of pathology from lower brainstem to neocortex. Later, animal studies showed that the hallmark protein of PD, α-synuclein, exhibited all the characteristics of a prion. Here, we describe our work using human neuroimaging to test the theory that PD pathology advances via a propagating process along the connectome. We found that the pattern and progression of brain atrophy follow neuronal connectivity, correlate with clinical features, and identify an epicenter in the brainstem.
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Affiliation(s)
- Alain Dagher
- Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
| | - Yashar Zeighami
- Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
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231
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Alzheimer's disease pathology propagation by exosomes containing toxic amyloid-beta oligomers. Acta Neuropathol 2018; 136:41-56. [PMID: 29934873 PMCID: PMC6015111 DOI: 10.1007/s00401-018-1868-1] [Citation(s) in RCA: 308] [Impact Index Per Article: 51.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2017] [Revised: 05/15/2018] [Accepted: 05/19/2018] [Indexed: 01/24/2023]
Abstract
The gradual deterioration of cognitive functions in Alzheimer’s disease is paralleled by a hierarchical progression of amyloid-beta and tau brain pathology. Recent findings indicate that toxic oligomers of amyloid-beta may cause propagation of pathology in a prion-like manner, although the underlying mechanisms are incompletely understood. Here we show that small extracellular vesicles, exosomes, from Alzheimer patients’ brains contain increased levels of amyloid-beta oligomers and can act as vehicles for the neuron-to-neuron transfer of such toxic species in recipient neurons in culture. Moreover, blocking the formation, secretion or uptake of exosomes was found to reduce both the spread of oligomers and the related toxicity. Taken together, our results imply that exosomes are centrally involved in Alzheimer’s disease and that they could serve as targets for development of new diagnostic and therapeutic principles.
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232
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Longitudinal Alzheimer’s Degeneration Reflects the Spatial Topography of Cholinergic Basal Forebrain Projections. Cell Rep 2018; 24:38-46. [DOI: 10.1016/j.celrep.2018.06.001] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2017] [Revised: 04/09/2018] [Accepted: 05/30/2018] [Indexed: 10/28/2022] Open
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Diaz-Parra A, Perez-Ramirez U, Pacheco-Torres J, Pfarr S, Sommer WH, Moratal D, Canals S. Evaluating network brain connectivity in alcohol postdependent state using Network-Based Statistic. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2017:533-536. [PMID: 29059927 DOI: 10.1109/embc.2017.8036879] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
The use of functional magnetic resonance imaging (fMRI) to measure spontaneous fluctuations in blood oxygen level dependent (BOLD) signals has become an indispensable tool to investigate how brain regions interact and form long-range networks. Statistical dependency measures between brain regions obtained from BOLD signals can inform about brain functional states in longitudinal studies of neurological and psychiatric disorders. Furthermore, its non-invasive nature allows comparable measurements in clinical and animal studies, providing excellent translational capabilities. In the present study, we apply Network-Based Statistic (NBS) to investigate alterations in the functional connectivity (FC) of the rat brain in a post-dependent (PD) state, an established animal model of clinical relevant features in alcoholism. In contrast to mass-univariate tests, in which comparisons are performed at single link-level, NBS enhances the statistical power by assuming that the connections comprising the effect of interest are interconnected. Brain-wide resting-state fMRI signals were collected in 14 controls and 13 PD rats, and Pearson correlations computed between 47 brain regions of interest (ROIs). The NBS analysis revealed statistically significant differences in a connected network of structures including hippocampus, amygdala, lateral hypothalamus and the raphe nucleus, all regions with known relevance for addictive behaviors. In contrast, no individual connection could be found significant by univariate comparisons with false discovery rate (FDR) correction. Correlations between the structures in the identified subnetwork tend to decrease or become negative (anti-correlated) in the PD state compared to controls. We interpret this result as evidence for a disconnected subnetwork in the PD state.
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Spatial Patterns of Hypometabolism and Amyloid Deposition in Variants of Alzheimer’s Disease Corresponding to Brain Networks: a Prospective Cohort Study. Mol Imaging Biol 2018; 21:140-148. [DOI: 10.1007/s11307-018-1219-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
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235
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Kale P, Zalesky A, Gollo LL. Estimating the impact of structural directionality: How reliable are undirected connectomes? Netw Neurosci 2018; 2:259-284. [PMID: 30234180 PMCID: PMC6135560 DOI: 10.1162/netn_a_00040] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2017] [Accepted: 12/19/2017] [Indexed: 11/30/2022] Open
Abstract
Directionality is a fundamental feature of network connections. Most structural brain networks are intrinsically directed because of the nature of chemical synapses, which comprise most neuronal connections. Because of the limitations of noninvasive imaging techniques, the directionality of connections between structurally connected regions of the human brain cannot be confirmed. Hence, connections are represented as undirected, and it is still unknown how this lack of directionality affects brain network topology. Using six directed brain networks from different species and parcellations (cat, mouse, C. elegans, and three macaque networks), we estimate the inaccuracies in network measures (degree, betweenness, clustering coefficient, path length, global efficiency, participation index, and small-worldness) associated with the removal of the directionality of connections. We employ three different methods to render directed brain networks undirected: (a) remove unidirectional connections, (b) add reciprocal connections, and (c) combine equal numbers of removed and added unidirectional connections. We quantify the extent of inaccuracy in network measures introduced through neglecting connection directionality for individual nodes and across the network. We find that the coarse division between core and peripheral nodes remains accurate for undirected networks. However, hub nodes differ considerably when directionality is neglected. Comparing the different methods to generate undirected networks from directed ones, we generally find that the addition of reciprocal connections (false positives) causes larger errors in graph-theoretic measures than the removal of the same number of directed connections (false negatives). These findings suggest that directionality plays an essential role in shaping brain networks and highlight some limitations of undirected connectomes.
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Affiliation(s)
- Penelope Kale
- QIMR Berghofer Medical Research Institute, Australia
- University of Queensland, Australia
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre and Department of Biomedical Engineering, University of Melbourne, Australia
| | - Leonardo L. Gollo
- QIMR Berghofer Medical Research Institute, Australia
- University of Queensland, Australia
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236
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Acosta D, Powell F, Zhao Y, Raj A. Regional vulnerability in Alzheimer's disease: The role of cell-autonomous and transneuronal processes. Alzheimers Dement 2018; 14:797-810. [PMID: 29306583 PMCID: PMC5994366 DOI: 10.1016/j.jalz.2017.11.014] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2017] [Revised: 11/08/2017] [Accepted: 11/30/2017] [Indexed: 01/21/2023]
Abstract
INTRODUCTION The stereotypical progression of Alzheimer's disease (AD) pathology is not fully understood. The selective impact of AD on distinct regions has led the field to question if innate vulnerability exists. This study aims to determine if the causative factors of regional vulnerability are dependent on cell-autonomous or transneuronal (non-cell autonomous) processes. METHODS Using mathematical and statistical models, we analyzed the contribution of cell-autonomous and non-cell autonomous factors to predictive linear models of AD pathology. RESULTS Results indicate gene expression as a weak contributor to predictive linear models of AD. Instead, the network diffusion model acts as a strong predictor of observed AD atrophy and hypometabolism. DISCUSSION We propose a convenient methodology for identifying genes and their role in determining AD topography, in comparison with network spread. Results reinforce the role of transneuronal network spread on disease progression and suggest that innate gene expression plays a secondary role in seeding and subsequent disease progression.
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Affiliation(s)
- Diana Acosta
- Department of Neuroscience, Weill Cornell Medical College of Cornell University, New York, NY, USA
| | - Fontasha Powell
- Department of Neuroscience, Weill Cornell Medical College of Cornell University, New York, NY, USA
| | - Yize Zhao
- Department of Healthcare Policy and Research, Weill Cornell Medical College of Cornell University, New York, NY, USA
| | - Ashish Raj
- Department of Neuroscience, Weill Cornell Medical College of Cornell University, New York, NY, USA; Department of Radiology and Biomedical Imaging, UCSF School of Medicine, San Francisco, CA, USA.
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237
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Baggio HC, Abos A, Segura B, Campabadal A, Garcia‐Diaz A, Uribe C, Compta Y, Marti MJ, Valldeoriola F, Junque C. Statistical inference in brain graphs using threshold-free network-based statistics. Hum Brain Mapp 2018; 39:2289-2302. [PMID: 29450940 PMCID: PMC6619254 DOI: 10.1002/hbm.24007] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2017] [Revised: 01/30/2018] [Accepted: 02/06/2018] [Indexed: 01/06/2023] Open
Abstract
The description of brain networks as graphs where nodes represent different brain regions and edges represent a measure of connectivity between a pair of nodes is an increasingly used approach in neuroimaging research. The development of powerful methods for edge-wise group-level statistical inference in brain graphs while controlling for multiple-testing associated false-positive rates, however, remains a difficult task. In this study, we use simulated data to assess the properties of threshold-free network-based statistics (TFNBS). The TFNBS combines threshold-free cluster enhancement, a method commonly used in voxel-wise statistical inference, and network-based statistic (NBS), which is frequently used for statistical analysis of brain graphs. Unlike the NBS, TFNBS generates edge-wise significance values and does not require the a priori definition of a hard cluster-defining threshold. Other test parameters, nonetheless, need to be set. We show that it is possible to find parameters that make TFNBS sensitive to strong and topologically clustered effects, while appropriately controlling false-positive rates. Our results show that the TFNBS is an adequate technique for the statistical assessment of brain graphs.
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Affiliation(s)
- Hugo C. Baggio
- Medical Psychology Unit, Department of Medicine, Institute of NeuroscienceUniversity of BarcelonaBarcelonaCataloniaSpain
| | - Alexandra Abos
- Medical Psychology Unit, Department of Medicine, Institute of NeuroscienceUniversity of BarcelonaBarcelonaCataloniaSpain
| | - Barbara Segura
- Medical Psychology Unit, Department of Medicine, Institute of NeuroscienceUniversity of BarcelonaBarcelonaCataloniaSpain
| | - Anna Campabadal
- Medical Psychology Unit, Department of Medicine, Institute of NeuroscienceUniversity of BarcelonaBarcelonaCataloniaSpain
| | - Anna Garcia‐Diaz
- Medical Psychology Unit, Department of Medicine, Institute of NeuroscienceUniversity of BarcelonaBarcelonaCataloniaSpain
| | - Carme Uribe
- Medical Psychology Unit, Department of Medicine, Institute of NeuroscienceUniversity of BarcelonaBarcelonaCataloniaSpain
| | - Yaroslau Compta
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Hospital Clínic de BarcelonaBarcelonaCataloniaSpain
- Movement Disorders Unit, Neurology Service, Hospital Clínic de Barcelona. Institute of Neuroscience, University of BarcelonaBarcelonaCataloniaSpain
| | - Maria Jose Marti
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Hospital Clínic de BarcelonaBarcelonaCataloniaSpain
- Movement Disorders Unit, Neurology Service, Hospital Clínic de Barcelona. Institute of Neuroscience, University of BarcelonaBarcelonaCataloniaSpain
| | - Francesc Valldeoriola
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Hospital Clínic de BarcelonaBarcelonaCataloniaSpain
- Movement Disorders Unit, Neurology Service, Hospital Clínic de Barcelona. Institute of Neuroscience, University of BarcelonaBarcelonaCataloniaSpain
| | - Carme Junque
- Medical Psychology Unit, Department of Medicine, Institute of NeuroscienceUniversity of BarcelonaBarcelonaCataloniaSpain
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Hospital Clínic de BarcelonaBarcelonaCataloniaSpain
- Institute of Biomedical Research August Pi i Sunyer (IDIBAPS)BarcelonaCataloniaSpain
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238
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Abdelnour F, Dayan M, Devinsky O, Thesen T, Raj A. Functional brain connectivity is predictable from anatomic network's Laplacian eigen-structure. Neuroimage 2018; 172:728-739. [PMID: 29454104 PMCID: PMC6170160 DOI: 10.1016/j.neuroimage.2018.02.016] [Citation(s) in RCA: 69] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2017] [Revised: 12/20/2017] [Accepted: 02/08/2018] [Indexed: 11/28/2022] Open
Abstract
How structural connectivity (SC) gives rise to functional connectivity (FC) is not fully understood. Here we mathematically derive a simple relationship between SC measured from diffusion tensor imaging, and FC from resting state fMRI. We establish that SC and FC are related via (structural) Laplacian spectra, whereby FC and SC share eigenvectors and their eigenvalues are exponentially related. This gives, for the first time, a simple and analytical relationship between the graph spectra of structural and functional networks. Laplacian eigenvectors are shown to be good predictors of functional eigenvectors and networks based on independent component analysis of functional time series. A small number of Laplacian eigenmodes are shown to be sufficient to reconstruct FC matrices, serving as basis functions. This approach is fast, and requires no time-consuming simulations. It was tested on two empirical SC/FC datasets, and was found to significantly outperform generative model simulations of coupled neural masses.
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Affiliation(s)
| | - Michael Dayan
- Radiology, Weill Cornell Medical College, New York, NY, USA
| | | | - Thomas Thesen
- Neurology, New York University, New York, NY, USA; Department of Physiology, Neuroscience & Behavioral Sciences, St. George's University, Grenada, West Indies
| | - Ashish Raj
- Radiology, Weill Cornell Medical College, New York, NY, USA
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239
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Gorges M, Müller HP, Kassubek J. Structural and Functional Brain Mapping Correlates of Impaired Eye Movement Control in Parkinsonian Syndromes: A Systems-Based Concept. Front Neurol 2018; 9:319. [PMID: 29867729 PMCID: PMC5949537 DOI: 10.3389/fneur.2018.00319] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2018] [Accepted: 04/23/2018] [Indexed: 01/18/2023] Open
Abstract
The investigation of the human oculomotor system by eye movement recordings provides an approach to behavior and its alterations in disease. The neurodegenerative process underlying parkinsonian syndromes, including Parkinson’s disease (PD), progressive supranuclear palsy (PSP), and multisystem atrophy (MSA) changes structural and functional brain organization, and thus affects eye movement control in a characteristic manner. Video-oculography has been established as a non-invasive recording device for eye movements, and systematic investigations of eye movement control in a clinical framework have emerged as a functional diagnostic tool in neurodegenerative parkinsonism. Disease-specific brain atrophy in parkinsonian syndromes has been reported for decades, these findings were refined by studies utilizing diffusion tensor imaging (DTI) and task-based/task-free functional MRI—both MRI techniques revealed disease-specific patterns of altered structural and functional brain organization. Here, characteristic disturbances of eye movement control in parkinsonian syndromes and their correlations with the structural and functional brain network alterations are reviewed. On this basis, we discuss the growing field of graph-based network analysis of the structural and functional connectome as a promising candidate for explaining abnormal phenotypes of eye movement control at the network level, both in health and in disease.
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Affiliation(s)
- Martin Gorges
- Department of Neurology, University of Ulm, Ulm, Germany
| | | | - Jan Kassubek
- Department of Neurology, University of Ulm, Ulm, Germany
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240
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Oxtoby NP, Young AL, Cash DM, Benzinger TLS, Fagan AM, Morris JC, Bateman RJ, Fox NC, Schott JM, Alexander DC. Data-driven models of dominantly-inherited Alzheimer's disease progression. Brain 2018; 141:1529-1544. [PMID: 29579160 PMCID: PMC5920320 DOI: 10.1093/brain/awy050] [Citation(s) in RCA: 83] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2017] [Revised: 11/23/2017] [Accepted: 01/06/2018] [Indexed: 11/16/2022] Open
Abstract
See Li and Donohue (doi:10.1093/brain/awy089) for a scientific commentary on this article.Dominantly-inherited Alzheimer's disease is widely hoped to hold the key to developing interventions for sporadic late onset Alzheimer's disease. We use emerging techniques in generative data-driven disease progression modelling to characterize dominantly-inherited Alzheimer's disease progression with unprecedented resolution, and without relying upon familial estimates of years until symptom onset. We retrospectively analysed biomarker data from the sixth data freeze of the Dominantly Inherited Alzheimer Network observational study, including measures of amyloid proteins and neurofibrillary tangles in the brain, regional brain volumes and cortical thicknesses, brain glucose hypometabolism, and cognitive performance from the Mini-Mental State Examination (all adjusted for age, years of education, sex, and head size, as appropriate). Data included 338 participants with known mutation status (211 mutation carriers in three subtypes: 163 PSEN1, 17 PSEN2, and 31 APP) and a baseline visit (age 19-66; up to four visits each, 1.1 ± 1.9 years in duration; spanning 30 years before, to 21 years after, parental age of symptom onset). We used an event-based model to estimate sequences of biomarker changes from baseline data across disease subtypes (mutation groups), and a differential equation model to estimate biomarker trajectories from longitudinal data (up to 66 mutation carriers, all subtypes combined). The two models concur that biomarker abnormality proceeds as follows: amyloid deposition in cortical then subcortical regions (∼24 ± 11 years before onset); phosphorylated tau (17 ± 8 years), tau and amyloid-β changes in cerebrospinal fluid; neurodegeneration first in the putamen and nucleus accumbens (up to 6 ± 2 years); then cognitive decline (7 ± 6 years), cerebral hypometabolism (4 ± 4 years), and further regional neurodegeneration. Our models predicted symptom onset more accurately than predictions that used familial estimates: root mean squared error of 1.35 years versus 5.54 years. The models reveal hidden detail on dominantly-inherited Alzheimer's disease progression, as well as providing data-driven systems for fine-grained patient staging and prediction of symptom onset with great potential utility in clinical trials.
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Affiliation(s)
- Neil P Oxtoby
- Progression of Neurodegenerative Disease Group, Centre for Medical Image Computing, Department of Computer Science, University College London, Gower Street, London WC1E 6BT, UK
| | - Alexandra L Young
- Progression of Neurodegenerative Disease Group, Centre for Medical Image Computing, Department of Computer Science, University College London, Gower Street, London WC1E 6BT, UK
| | - David M Cash
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, University College London, 8-11 Queen Square, London WC1N 3AR, UK
- Translational Imaging Group, Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, Gower Street, London WC1E 6BT, UK
| | - Tammie L S Benzinger
- Department of Neurology, Washington University School of Medicine, St Louis, MO, 63110, USA
| | - Anne M Fagan
- Department of Neurology, Washington University School of Medicine, St Louis, MO, 63110, USA
| | - John C Morris
- Department of Neurology, Washington University School of Medicine, St Louis, MO, 63110, USA
| | - Randall J Bateman
- Department of Neurology, Washington University School of Medicine, St Louis, MO, 63110, USA
| | - Nick C Fox
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, University College London, 8-11 Queen Square, London WC1N 3AR, UK
- UK Dementia Research Institute, University College London, London, UK
| | - Jonathan M Schott
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, University College London, 8-11 Queen Square, London WC1N 3AR, UK
| | - Daniel C Alexander
- Progression of Neurodegenerative Disease Group, Centre for Medical Image Computing, Department of Computer Science, University College London, Gower Street, London WC1E 6BT, UK
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241
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Cauda F, Nani A, Costa T, Palermo S, Tatu K, Manuello J, Duca S, Fox PT, Keller R. The morphometric co-atrophy networking of schizophrenia, autistic and obsessive spectrum disorders. Hum Brain Mapp 2018; 39:1898-1928. [PMID: 29349864 PMCID: PMC5895505 DOI: 10.1002/hbm.23952] [Citation(s) in RCA: 42] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2017] [Revised: 12/19/2017] [Accepted: 12/28/2017] [Indexed: 12/13/2022] Open
Abstract
By means of a novel methodology that can statistically derive patterns of co-alterations distribution from voxel-based morphological data, this study analyzes the patterns of brain alterations of three important psychiatric spectra-that is, schizophrenia spectrum disorder (SCZD), autistic spectrum disorder (ASD), and obsessive-compulsive spectrum disorder (OCSD). Our analysis provides five important results. First, in SCZD, ASD, and OCSD brain alterations do not distribute randomly but, rather, follow network-like patterns of co-alteration. Second, the clusters of co-altered areas form a net of alterations that can be defined as morphometric co-alteration network or co-atrophy network (in the case of gray matter decreases). Third, within this network certain cerebral areas can be identified as pathoconnectivity hubs, the alteration of which is supposed to enhance the development of neuronal abnormalities. Fourth, within the morphometric co-atrophy network of SCZD, ASD, and OCSD, a subnetwork composed of eleven highly connected nodes can be distinguished. This subnetwork encompasses the anterior insulae, inferior frontal areas, left superior temporal areas, left parahippocampal regions, left thalamus and right precentral gyri. Fifth, the co-altered areas also exhibit a normal structural covariance pattern which overlaps, for some of these areas (like the insulae), the co-alteration pattern. These findings reveal that, similarly to neurodegenerative diseases, psychiatric disorders are characterized by anatomical alterations that distribute according to connectivity constraints so as to form identifiable morphometric co-atrophy patterns.
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Affiliation(s)
- Franco Cauda
- GCS‐FMRI, Koelliker Hospital and Department of PsychologyUniversity of TurinTurinItaly
- Focus Lab, Department of PsychologyUniversity of TurinTurinItaly
| | - Andrea Nani
- GCS‐FMRI, Koelliker Hospital and Department of PsychologyUniversity of TurinTurinItaly
- Focus Lab, Department of PsychologyUniversity of TurinTurinItaly
- Michael Trimble Neuropsychiatry Research Group, University of Birmingham and BSMHFTBirminghamUK
| | - Tommaso Costa
- GCS‐FMRI, Koelliker Hospital and Department of PsychologyUniversity of TurinTurinItaly
- Focus Lab, Department of PsychologyUniversity of TurinTurinItaly
| | - Sara Palermo
- Department of NeuroscienceUniversity of TurinTurinItaly
| | - Karina Tatu
- GCS‐FMRI, Koelliker Hospital and Department of PsychologyUniversity of TurinTurinItaly
- Focus Lab, Department of PsychologyUniversity of TurinTurinItaly
| | - Jordi Manuello
- GCS‐FMRI, Koelliker Hospital and Department of PsychologyUniversity of TurinTurinItaly
- Focus Lab, Department of PsychologyUniversity of TurinTurinItaly
| | - Sergio Duca
- GCS‐FMRI, Koelliker Hospital and Department of PsychologyUniversity of TurinTurinItaly
| | - Peter T. Fox
- Research Imaging Institute, University of Texas Health Science Center At San AntonioSan AntonioTexas
- South Texas Veterans Health Care SystemSan AntonioTexas
| | - Roberto Keller
- Adult Autism Center, DSM Local Health Unit ASL Citta’ Di TorinoTurinItaly
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242
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Hane FT, Lee BY, Leonenko Z. Recent Progress in Alzheimer's Disease Research, Part 1: Pathology. J Alzheimers Dis 2018; 57:1-28. [PMID: 28222507 DOI: 10.3233/jad-160882] [Citation(s) in RCA: 65] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
The field of Alzheimer's disease (AD) research has grown exponentially over the past few decades, especially since the isolation and identification of amyloid-β from postmortem examination of the brains of AD patients. Recently, the Journal of Alzheimer's Disease (JAD) put forth approximately 300 research reports which were deemed to be the most influential research reports in the field of AD since 2010. JAD readers were asked to vote on these most influential reports. In this 3-part review, we review the results of the 300 most influential AD research reports to provide JAD readers with a readily accessible, yet comprehensive review of the state of contemporary research. Notably, this multi-part review identifies the "hottest" fields of AD research providing guidance for both senior investigators as well as investigators new to the field on what is the most pressing fields within AD research. Part 1 of this review covers pathogenesis, both on a molecular and macro scale. Part 2 review genetics and epidemiology, and part 3 covers diagnosis and treatment. This part of the review, pathology, reviews amyloid-β, tau, prions, brain structure, and functional changes with AD and the neuroimmune response of AD.
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Affiliation(s)
- Francis T Hane
- Department of Biology, University of Waterloo, Waterloo, ON, Canada.,Department of Chemistry, Lakehead University, Thunder Bay, ON, Canada
| | - Brenda Y Lee
- Department of Biology, University of Waterloo, Waterloo, ON, Canada
| | - Zoya Leonenko
- Department of Biology, University of Waterloo, Waterloo, ON, Canada.,Department of Physics and Astronomy, University of Waterloo, Waterloo, ON, Canada
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243
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Pandya S, Kuceyeski A, Raj A. The Brain's Structural Connectome Mediates the Relationship between Regional Neuroimaging Biomarkers in Alzheimer's Disease. J Alzheimers Dis 2018; 55:1639-1657. [PMID: 27911289 DOI: 10.3233/jad-160090] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Alzheimer's disease (AD), one of the most common causes of dementia in adults, is a progressive neurodegenerative disorder exhibiting well-defined neuropathological hallmarks. It is known that disease pathology involves misfolded amyloid-β (Aβ) and tau proteins, and exhibits a relatively stereotyped progression over decades. The relationship between AD neuropathological hallmarks (Aβ, hypometabolism, and tau proteins) and imaging biomarkers (MRI, AV-45/FDG-PET) is not fully understood. In addition, biomarker pathologies are oftentimes discordant, wherein it may show varying levels of abnormality across brain regions. Evidence based on recent elucidation of trans-neuronal "prion-like" transmission and other available data already suggests that disease spread follows the brain's fiber connectivity network. Thereby, the brain's connectome information can be used to predict the process of disease spread in AD. A recently established mathematical model of AD pathology spread using a connectome-based network diffusion model was successful in encapsulating neurodegenerative progression. Motivated by these network-based findings, the current study explores whether and how network connectivity mediates the interactions between various AD biomarkers. We hypothesized that the structural connectivity matrix will mediate the cross-sectional association between regional AD-associated hypometabolism and Aβ deposition. Given recent reports of inherent or lifetime activity of brain regions as strong predictors of Aβ deposition in patients, we also tested whether healthy metabolism exerts a network-mediated effect on Aβ deposition and hypometabolism in AD patients. We found that regional Aβ deposition is best predicted by a linear combination of both regional healthy local metabolism and connectome-mediated regional healthy metabolism.
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244
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Tai XY, Bernhardt B, Thom M, Thompson P, Baxendale S, Koepp M, Bernasconi N. Review: Neurodegenerative processes in temporal lobe epilepsy with hippocampal sclerosis: Clinical, pathological and neuroimaging evidence. Neuropathol Appl Neurobiol 2018; 44:70-90. [DOI: 10.1111/nan.12458] [Citation(s) in RCA: 58] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2017] [Accepted: 12/07/2017] [Indexed: 12/14/2022]
Affiliation(s)
- X. Y. Tai
- Division of Neuropathology and Department of Clinical and Experimental Epilepsy; UCL Institute of Neurology; London UK
| | - B. Bernhardt
- Neuroimaging of Epilepsy Laboratory; McConnell Brain Imaging Centre; Montreal Neurological Institute; McGill University; Montreal Quebec Canada
- Multimodal Imaging and Connectome Analysis Lab; Montreal Neurological Institute; Montreal Neurological Institute; McGill University; Montreal Quebec Canada
| | - M. Thom
- Division of Neuropathology and Department of Clinical and Experimental Epilepsy; UCL Institute of Neurology; London UK
| | - P. Thompson
- Department of Clinical and Experimental Epilepsy; UCL Institute of Neurology; London UK
| | - S. Baxendale
- Department of Clinical and Experimental Epilepsy; UCL Institute of Neurology; London UK
| | - M. Koepp
- Department of Clinical and Experimental Epilepsy; UCL Institute of Neurology; London UK
| | - N. Bernasconi
- Neuroimaging of Epilepsy Laboratory; McConnell Brain Imaging Centre; Montreal Neurological Institute; McGill University; Montreal Quebec Canada
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245
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Carbonell F, Iturria-Medina Y, Evans AC. Mathematical Modeling of Protein Misfolding Mechanisms in Neurological Diseases: A Historical Overview. Front Neurol 2018; 9:37. [PMID: 29456521 PMCID: PMC5801313 DOI: 10.3389/fneur.2018.00037] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2017] [Accepted: 01/16/2018] [Indexed: 12/12/2022] Open
Abstract
Protein misfolding refers to a process where proteins become structurally abnormal and lose their specific 3-dimensional spatial configuration. The histopathological presence of misfolded protein (MP) aggregates has been associated as the primary evidence of multiple neurological diseases, including Prion diseases, Alzheimer's disease, Parkinson's disease, and Creutzfeldt-Jacob disease. However, the exact mechanisms of MP aggregation and propagation, as well as their impact in the long-term patient's clinical condition are still not well understood. With this aim, a variety of mathematical models has been proposed for a better insight into the kinetic rate laws that govern the microscopic processes of protein aggregation. Complementary, another class of large-scale models rely on modern molecular imaging techniques for describing the phenomenological effects of MP propagation over the whole brain. Unfortunately, those neuroimaging-based studies do not take full advantage of the tremendous capabilities offered by the chemical kinetics modeling approach. Actually, it has been barely acknowledged that the vast majority of large-scale models have foundations on previous mathematical approaches that describe the chemical kinetics of protein replication and propagation. The purpose of the current manuscript is to present a historical review about the development of mathematical models for describing both microscopic processes that occur during the MP aggregation and large-scale events that characterize the progression of neurodegenerative MP-mediated diseases.
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Affiliation(s)
| | - Yasser Iturria-Medina
- Department of Neurology & Neurosurgery, McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, QC, Canada
- Ludmer Centre for NeuroInformatics and Mental Health, Montreal, QC, Canada
| | - Alan C. Evans
- Department of Neurology & Neurosurgery, McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, QC, Canada
- Ludmer Centre for NeuroInformatics and Mental Health, Montreal, QC, Canada
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246
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Cope TE, Rittman T, Borchert RJ, Jones PS, Vatansever D, Allinson K, Passamonti L, Vazquez Rodriguez P, Bevan-Jones WR, O'Brien JT, Rowe JB. Tau burden and the functional connectome in Alzheimer's disease and progressive supranuclear palsy. Brain 2018; 141:550-567. [PMID: 29293892 PMCID: PMC5837359 DOI: 10.1093/brain/awx347] [Citation(s) in RCA: 162] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2017] [Revised: 10/25/2017] [Accepted: 10/29/2017] [Indexed: 12/21/2022] Open
Abstract
Alzheimer's disease and progressive supranuclear palsy (PSP) represent neurodegenerative tauopathies with predominantly cortical versus subcortical disease burden. In Alzheimer's disease, neuropathology and atrophy preferentially affect 'hub' brain regions that are densely connected. It was unclear whether hubs are differentially affected by neurodegeneration because they are more likely to receive pathological proteins that propagate trans-neuronally, in a prion-like manner, or whether they are selectively vulnerable due to a lack of local trophic factors, higher metabolic demands, or differential gene expression. We assessed the relationship between tau burden and brain functional connectivity, by combining in vivo PET imaging using the ligand AV-1451, and graph theoretic measures of resting state functional MRI in 17 patients with Alzheimer's disease, 17 patients with PSP, and 12 controls. Strongly connected nodes displayed more tau pathology in Alzheimer's disease, independently of intrinsic connectivity network, validating the predictions of theories of trans-neuronal spread but not supporting a role for metabolic demands or deficient trophic support in tau accumulation. This was not a compensatory phenomenon, as the functional consequence of increasing tau burden in Alzheimer's disease was a progressive weakening of the connectivity of these same nodes, reducing weighted degree and local efficiency and resulting in weaker 'small-world' properties. Conversely, in PSP, unlike in Alzheimer's disease, those nodes that accrued pathological tau were those that displayed graph metric properties associated with increased metabolic demand and a lack of trophic support rather than strong functional connectivity. Together, these findings go some way towards explaining why Alzheimer's disease affects large scale connectivity networks throughout cortex while neuropathology in PSP is concentrated in a small number of subcortical structures. Further, we demonstrate that in PSP increasing tau burden in midbrain and deep nuclei was associated with strengthened cortico-cortical functional connectivity. Disrupted cortico-subcortical and cortico-brainstem interactions meant that information transfer took less direct paths, passing through a larger number of cortical nodes, reducing closeness centrality and eigenvector centrality in PSP, while increasing weighted degree, clustering, betweenness centrality and local efficiency. Our results have wide-ranging implications, from the validation of models of tau trafficking in humans to understanding the relationship between regional tau burden and brain functional reorganization.
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Affiliation(s)
- Thomas E Cope
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Timothy Rittman
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Robin J Borchert
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - P Simon Jones
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Deniz Vatansever
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- Department of Psychology, University of York, York, UK
- Division of Anaesthesia, Department of Medicine, University of Cambridge, Cambridge, UK
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Kieren Allinson
- Department of Pathology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Luca Passamonti
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | | | - W Richard Bevan-Jones
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - John T O'Brien
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - James B Rowe
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- Medical Research Council Cognition and Brain Sciences Unit, Cambridge, UK
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247
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Freeze B, Acosta D, Pandya S, Zhao Y, Raj A. Regional expression of genes mediating trans-synaptic alpha-synuclein transfer predicts regional atrophy in Parkinson disease. NEUROIMAGE-CLINICAL 2018; 18:456-466. [PMID: 29868450 PMCID: PMC5984599 DOI: 10.1016/j.nicl.2018.01.009] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/11/2017] [Revised: 01/04/2018] [Accepted: 01/13/2018] [Indexed: 11/09/2022]
Abstract
Multiple genes have been implicated in Parkinson disease pathogenesis, but the relationship between regional expression of these genes and regional dysfunction across the brain is unknown. We address this question by joint analysis of high resolution magnetic resonance imaging data from the Parkinson's Progression Markers Initiative and regional genetic microarray expression data from the Allen Brain Atlas. Regional brain atrophy and genetic expression was co-registered to a common 86 region brain atlas and robust multivariable regression analysis was performed to identify genetic predictors of regional brain atrophy. Top candidate genes from GWAS analysis, as well as genes implicated in trans-synaptic alpha-synuclein transfer and autosomal recessive PD were included in our analysis. We identify three genes with expression patterns that are highly significant predictors of regional brain atrophy. The two most significant predictors are LAG3 and RAB5A, genes implicated in trans-synaptic synuclein transfer. Other well-validated PD-related genes do not have expression patterns that predict regional atrophy, suggesting that they may serve other roles such as disease initiation factors. Joint volumetric and microarray analysis identifies gene expression patterns that predict the PD atrophy pattern. The most highly predictive genes, LAG3 and RAB5A, are implicated in trans-synaptic alpha-synuclein transfer. The expression patterns of alpha-synuclein and otherPD-related genes do not predict atrophy.
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Affiliation(s)
- Benjamin Freeze
- Department of Radiology, NewYork-Presbyterian Hospital/Weill Cornell Medicine, United States.
| | - Diana Acosta
- Department of Radiology, NewYork-Presbyterian Hospital/Weill Cornell Medicine, United States
| | - Sneha Pandya
- Department of Radiology, NewYork-Presbyterian Hospital/Weill Cornell Medicine, United States
| | - Yize Zhao
- Division of Biostatistics and Epidemiology, Department of Healthcare Policy and Research, Weill Cornell Medicine, United States
| | - Ashish Raj
- Department of Radiology, NewYork-Presbyterian Hospital/Weill Cornell Medicine, United States; Department of Radiology, University of California, San Francisco, United States
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248
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Neitzel J, Nuttall R, Sorg C. Perspectives on How Human Simultaneous Multi-Modal Imaging Adds Directionality to Spread Models of Alzheimer's Disease. Front Neurol 2018; 9:26. [PMID: 29434570 PMCID: PMC5790782 DOI: 10.3389/fneur.2018.00026] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2017] [Accepted: 01/12/2018] [Indexed: 12/31/2022] Open
Abstract
Previous animal research suggests that the spread of pathological agents in Alzheimer’s disease (AD) follows the direction of signaling pathways. Specifically, tau pathology has been suggested to propagate in an infection-like mode along axons, from transentorhinal cortices to medial temporal lobe cortices and consequently to other cortical regions, while amyloid-beta (Aβ) pathology seems to spread in an activity-dependent manner among and from isocortical regions into limbic and then subcortical regions. These directed connectivity-based spread models, however, have not been tested directly in AD patients due to the lack of an in vivo method to identify directed connectivity in humans. Recently, a new method—metabolic connectivity mapping (MCM)—has been developed and validated in healthy participants that uses simultaneous FDG-PET and resting-state fMRI data acquisition to identify directed intrinsic effective connectivity (EC). To this end, postsynaptic energy consumption (FDG-PET) is used to identify regions with afferent input from other functionally connected brain regions (resting-state fMRI). Here, we discuss how this multi-modal imaging approach allows quantitative, whole-brain mapping of signaling direction in AD patients, thereby pointing out some of the advantages it offers compared to other EC methods (i.e., Granger causality, dynamic causal modeling, Bayesian networks). Most importantly, MCM provides the basis on which models of pathology spread, derived from animal studies, can be tested in AD patients. In particular, future work should investigate whether tau and Aβ in humans propagate along the trajectories of directed connectivity in order to advance our understanding of the neuropathological mechanisms causing disease progression.
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Affiliation(s)
- Julia Neitzel
- Department of General and Experimental Psychology, Ludwig-Maximilians-Universität (LMU), München, Germany.,TUM-Neuroimaging Center (TUM-NIC), Klinikum rechts der Isar, Technische Universität München (TUM), München, Germany
| | - Rachel Nuttall
- TUM-Neuroimaging Center (TUM-NIC), Klinikum rechts der Isar, Technische Universität München (TUM), München, Germany.,Department of Neuroradiology, Klinikum rechts der Isar, Technische Universität München (TUM), München, Germany
| | - Christian Sorg
- TUM-Neuroimaging Center (TUM-NIC), Klinikum rechts der Isar, Technische Universität München (TUM), München, Germany.,Department of Neuroradiology, Klinikum rechts der Isar, Technische Universität München (TUM), München, Germany.,Department of Psychiatry and Psychotherapy, Klinikum rechts der Isar, Technische Universität München (TUM), München, Germany
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249
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Manuello J, Nani A, Premi E, Borroni B, Costa T, Tatu K, Liloia D, Duca S, Cauda F. The Pathoconnectivity Profile of Alzheimer's Disease: A Morphometric Coalteration Network Analysis. Front Neurol 2018; 8:739. [PMID: 29472885 PMCID: PMC5810291 DOI: 10.3389/fneur.2017.00739] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2017] [Accepted: 12/21/2017] [Indexed: 01/18/2023] Open
Abstract
Gray matter alterations are typical features of brain disorders. However, they do not impact on the brain randomly. Indeed, it has been suggested that neuropathological processes can selectively affect certain assemblies of neurons, which typically are at the center of crucial functional networks. Because of their topological centrality, these areas form a core set that is more likely to be affected by neuropathological processes. In order to identify and study the pattern formed by brain alterations in patients’ with Alzheimer’s disease (AD), we devised an innovative meta-analytic method for analyzing voxel-based morphometry data. This methodology enabled us to discover that in AD gray matter alterations do not occur randomly across the brain but, on the contrary, follow identifiable patterns of distribution. This alteration pattern exhibits a network-like structure composed of coaltered areas that can be defined as coatrophy network. Within the coatrophy network of AD, we were able to further identify a core subnetwork of coaltered areas that includes the left hippocampus, left and right amygdalae, right parahippocampal gyrus, and right temporal inferior gyrus. In virtue of their network centrality, these brain areas can be thought of as pathoconnectivity hubs.
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Affiliation(s)
- Jordi Manuello
- GCS-fMRI, Department of Psychology, Koelliker Hospital, University of Turin, Turin, Italy.,FOCUS Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Andrea Nani
- GCS-fMRI, Department of Psychology, Koelliker Hospital, University of Turin, Turin, Italy.,FOCUS Laboratory, Department of Psychology, University of Turin, Turin, Italy.,Michael Trimble Neuropsychiatry Research Group, Birmingham and Solihull Mental Health NHS Foundation Trust, Birmingham, United Kingdom
| | - Enrico Premi
- Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Barbara Borroni
- Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Tommaso Costa
- GCS-fMRI, Department of Psychology, Koelliker Hospital, University of Turin, Turin, Italy.,FOCUS Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Karina Tatu
- GCS-fMRI, Department of Psychology, Koelliker Hospital, University of Turin, Turin, Italy.,FOCUS Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Donato Liloia
- FOCUS Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Sergio Duca
- GCS-fMRI, Department of Psychology, Koelliker Hospital, University of Turin, Turin, Italy
| | - Franco Cauda
- GCS-fMRI, Department of Psychology, Koelliker Hospital, University of Turin, Turin, Italy.,FOCUS Laboratory, Department of Psychology, University of Turin, Turin, Italy
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250
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Becker CO, Pequito S, Pappas GJ, Miller MB, Grafton ST, Bassett DS, Preciado VM. Spectral mapping of brain functional connectivity from diffusion imaging. Sci Rep 2018; 8:1411. [PMID: 29362436 PMCID: PMC5780460 DOI: 10.1038/s41598-017-18769-x] [Citation(s) in RCA: 48] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2017] [Accepted: 12/15/2017] [Indexed: 01/22/2023] Open
Abstract
Understanding the relationship between the dynamics of neural processes and the anatomical substrate of the brain is a central question in neuroscience. On the one hand, modern neuroimaging technologies, such as diffusion tensor imaging, can be used to construct structural graphs representing the architecture of white matter streamlines linking cortical and subcortical structures. On the other hand, temporal patterns of neural activity can be used to construct functional graphs representing temporal correlations between brain regions. Although some studies provide evidence that whole-brain functional connectivity is shaped by the underlying anatomy, the observed relationship between function and structure is weak, and the rules by which anatomy constrains brain dynamics remain elusive. In this article, we introduce a methodology to map the functional connectivity of a subject at rest from his or her structural graph. Using our methodology, we are able to systematically account for the role of structural walks in the formation of functional correlations. Furthermore, in our empirical evaluations, we observe that the eigenmodes of the mapped functional connectivity are associated with activity patterns associated with different cognitive systems.
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Affiliation(s)
- Cassiano O Becker
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, USA
| | - Sérgio Pequito
- Department of Industrial and Systems Engineering, Rensselaer Polytechnic Institute, Troy, USA
| | - George J Pappas
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, USA
| | - Michael B Miller
- Department of Psychological and Brain Sciences, University of California at Santa Barbara, Santa Barbara, USA
| | - Scott T Grafton
- Department of Psychological and Brain Sciences, University of California at Santa Barbara, Santa Barbara, USA
| | - Danielle S Bassett
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, USA.,Department of Bioengineering, University of Pennsylvania, Philadelphia, USA
| | - Victor M Preciado
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, USA.
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