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1
Longitudinal white matter change in frontotemporal dementia subtypes and sporadic late onset Alzheimer's disease.
Elahi, Fanny M; Marx, Gabe; Cobigo, Yann. - : eScholarship, University of California, 2017
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2
Data-driven classification of patients with primary progressive aphasia
In: Hoffman, P; Sajjadi, SA; Patterson, K; & Nestor, PJ. (2017). Data-driven classification of patients with primary progressive aphasia. BRAIN AND LANGUAGE, 174, 86 - 93. doi:10.1016/j.bandl.2017.08.001. UC Irvine: Retrieved from: http://www.escholarship.org/uc/item/5m4842c0 (2017)
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3
Longitudinal white matter change in frontotemporal dementia subtypes and sporadic late onset Alzheimer's disease.
Elahi, Fanny M; Marx, Gabe; Cobigo, Yann. - : eScholarship, University of California, 2017
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4
Data-driven classification of patients with primary progressive aphasia.
Nestor, Peter J; Sajjadi, Seyed Ahmad; Patterson, Karalyn. - : eScholarship, University of California, 2017
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5
Data-driven regions of interest for longitudinal change in three variants of frontotemporal lobar degeneration
In: Binney, RJ; Pankov, A; Marx, G; He, X; McKenna, F; Staffaroni, AM; et al.(2017). Data-driven regions of interest for longitudinal change in three variants of frontotemporal lobar degeneration. BRAIN AND BEHAVIOR, 7(4), e00675. doi:10.1002/brb3.675. UCSF: Retrieved from: http://www.escholarship.org/uc/item/1xd9f3hr (2017)
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6
A neural network underlying intentional emotional facial expression in neurodegenerative disease.
Gola, Kelly A; Shany-Ur, Tal; Pressman, Peter. - : eScholarship, University of California, 2017
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7
A neural network underlying intentional emotional facial expression in neurodegenerative disease.
Gola, Kelly A; Shany-Ur, Tal; Pressman, Peter. - : eScholarship, University of California, 2017
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8
A neural network underlying intentional emotional facial expression in neurodegenerative disease
In: Gola, KA; Shany-Ur, T; Pressman, P; Sulman, I; Galeana, E; Paulsen, H; et al.(2017). A neural network underlying intentional emotional facial expression in neurodegenerative disease. NEUROIMAGE-CLINICAL, 14, 672 - 678. doi:10.1016/j.nicl.2017.01.016. UCSF: Retrieved from: http://www.escholarship.org/uc/item/9kp6g3dm (2017)
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9
Data-driven regions of interest for longitudinal change in three variants of frontotemporal lobar degeneration.
In: Brain and behavior, vol 7, iss 4 (2017)
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10
Observing conversational laughter in frontotemporal dementia.
In: Journal of neurology, neurosurgery, and psychiatry, vol 88, iss 5 (2017)
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11
Emotion detection deficits and changes in personality traits linked to loss of white matter integrity in primary progressive aphasia.
Multani, Namita; Galantucci, Sebastiano; Wilson, Stephen M. - : eScholarship, University of California, 2017
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12
Observing conversational laughter in frontotemporal dementia.
In: Journal of neurology, neurosurgery, and psychiatry, vol 88, iss 5 (2017)
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13
Data-driven regions of interest for longitudinal change in three variants of frontotemporal lobar degeneration.
In: Brain and behavior, vol 7, iss 4 (2017)
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14
Artificial grammar learning in vascular and progressive non-fluent aphasias ...
Cope, Thomas; Wilson, B; Robson, H. - : Apollo - University of Cambridge Repository, 2017
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15
Clinicopathologic correlations and neuroimaging biomarkers in primary progressive aphasia
Santos Santos, Miguel Ángel. - : Universitat Autònoma de Barcelona, 2017
In: TDX (Tesis Doctorals en Xarxa) (2017)
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16
The TMEM106B risk allele is associated with lower cortical volumes in a clinically diagnosed frontotemporal dementia cohort.
In: J Neurol Neurosurg Psychiatry (2017) (2017)
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17
Artificial grammar learning in vascular and progressive non-fluent aphasias
Cope, Thomas; Wilson, B; Robson, H. - : Elsevier, 2017. : Neuropsychologia, 2017
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18
Data-driven classification of patients with primary progressive aphasia
Abstract: Current diagnostic criteria classify primary progressive aphasia into three variants–semantic (sv), nonfluent (nfv) and logopenic (lv) PPA–though the adequacy of this scheme is debated. This study took a data-driven approach, applying k-means clustering to data from 43 PPA patients. The algorithm grouped patients based on similarities in language, semantic and non-linguistic cognitive scores. The optimum solution consisted of three groups. One group, almost exclusively those diagnosed as svPPA, displayed a selective semantic impairment. A second cluster, with impairments to speech production, repetition and syntactic processing, contained a majority of patients with nfvPPA but also some lvPPA patients. The final group exhibited more severe deficits to speech, repetition and syntax as well as semantic and other cognitive deficits. These results suggest that, amongst cases of non-semantic PPA, differentiation mainly reflects overall degree of language/cognitive impairment. The observed patterns were scarcely affected by inclusion/exclusion of non-linguistic cognitive scores.
Keyword: 1203 Language and Linguistics; 2805 Cognitive Neuroscience; 3205 Experimental and Cognitive Psychology; 3310 Linguistics and Language; 3616 Speech and Hearing; Alzheimer's disease; Frontotemporal dementia; Logopenic aphasia; Non-fluent aphasia; Primary progressive aphasia; Semantic dementia
URL: https://espace.library.uq.edu.au/view/UQ:657338e
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19
Can MRI visual assessment differentiate the variants of primary-progressive aphasia?
Sajjadi, S. A.; Sheikh-Bahaei, N.; Cross, J.. - : American Society of Neuroradiology, 2017
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