Neologistic jargon aphasia and agraphia in primary progressive aphasia

The terms 'jargon aphasia' and 'jargon agraphia' describe the production of incomprehensible language containing frequent phonological, semantic or neologistic errors in speech and writing, respectively. Here we describe two patients with primary progressive aphasia (PPA) who produced neologistic jargon either in speech or writing. We suggest that involvement of the posterior superior temporal-inferior parietal region may lead to a disconnection between stored lexical representations and language output pathways leading to aberrant activation of phonemes in neologistic jargon. Parietal lobe involvement is relatively unusual in PPA, perhaps accounting for the comparative rarity of jargon early in the course of these diseases. (C) 2008 Elsevier B.V. All rights reserved

The undiscovered syndrome: Macdonald Critchley's case of semantic dementia

Semantic dementia is a unique clinicopathological syndrome in the frontotemporal lobar degeneration spectrum. It is characterized by progressive and relatively selective impairment of semantic memory, associated with asymmetric antero-inferior temporal lobe atrophy. Although the syndrome became widely recognized only in the 1980s, descriptions of cases with typical features of semantic dementia have been on record for over a century. Here, we draw attention to a well documented historical case of a patient with features that would have fulfilled current consensus criteria for semantic dementia, as reconstructed from the notes made by her neurologist, Macdonald Critchley, in 1938. This case raises a number of issues concerning the nosology of the semantic dementia syndrome and the potential value of archived case material

An information theoretic characterisation of auditory encoding.

The entropy metric derived from information theory provides a means to quantify the amount of information transmitted in acoustic streams like speech or music. By systematically varying the entropy of pitch sequences, we sought brain areas where neural activity and energetic demands increase as a function of entropy. Such a relationship is predicted to occur in an efficient encoding mechanism that uses less computational resource when less information is present in the signal: we specifically tested the hypothesis that such a relationship is present in the planum temporale (PT). In two convergent functional MRI studies, we demonstrated this relationship in PT for encoding, while furthermore showing that a distributed fronto-parietal network for retrieval of acoustic information is independent of entropy. The results establish PT as an efficient neural engine that demands less computational resource to encode redundant signals than those with high information content

Data processing of remotely sensed airborne hyperspectral data using the Airborne Processing Library (APL): Geocorrection algorithm descriptions and spatial accuracy assessment

This is the author's preprint. The final version is available from the publisher via the DOI in this record.The authors would like to thank Dr. Peter Land for useful discussions on reflectance spectra of ground targets. Fig. 9 contains Ordnance Survey OpenData © Crown copyright and database right 2013. The hyperspectral data used in this report were collected by the Natural Environment Research Council Airborne Research and Survey Facility.Remote sensing airborne hyperspectral data are routinely used for applications including algorithm development for satellite sensors, environmental monitoring and atmospheric studies. Single flight lines of airborne hyperspectral data are often in the region of tens of gigabytes in size. This means that a single aircraft can collect terabytes of remotely sensed hyperspectral data during a single year. Before these data can be used for scientific analyses, they need to be radiometrically calibrated, synchronised with the aircraft's position and attitude and then geocorrected. To enable efficient processing of these large datasets the UK Airborne Research and Survey Facility has recently developed a software suite, the Airborne Processing Library (APL), for processing airborne hyperspectral data acquired from the Specim AISA Eagle and Hawk instruments. The APL toolbox allows users to radiometrically calibrate, geocorrect, reproject and resample airborne data. Each stage of the toolbox outputs data in the common Band Interleaved Lines (BILs) format, which allows its integration with other standard remote sensing software packages. APL was developed to be user-friendly and suitable for use on a workstation PC as well as for the automated processing of the facility; to this end APL can be used under both Windows and Linux environments on a single desktop machine or through a Grid engine. A graphical user interface also exists. In this paper we describe the Airborne Processing Library software, its algorithms and approach. We present example results from using APL with an AISA Eagle sensor and we assess its spatial accuracy using data from multiple flight lines collected during a campaign in 2008 together with in situ surveyed ground control points. © 2013 Elsevier Ltd

Computational modelling of pathogenic protein spread in neurodegenerative diseases

Pathogenic protein accumulation and spread are fundamental principles of neurodegenerative diseases and ultimately account for the atrophy patterns that distinguish these diseases clinically. However, the biological mechanisms that link pathogenic proteins to specific neural network damage patterns have not been defined. We developed computational models for mechanisms of pathogenic protein accumulation, spread and toxic effects in an artificial neural network of cortical columns. By varying simulation parameters we assessed the effects of modelled mechanisms on network breakdown patterns. Our findings suggest that patterns of network breakdown and the convergence of patterns follow rules determined by particular protein parameters. These rules can account for empirical data on pathogenic protein spread in neural networks. This work provides a basis for understanding the effects of pathogenic proteins on neural circuits and predicting progression of neurodegeneration

Patterns of regional cerebellar atrophy in genetic frontotemporal dementia

BACKGROUND: Frontotemporal dementia (FTD) is a heterogeneous neurodegenerative disorder with a strong genetic component. The cerebellum has not traditionally been felt to be involved in FTD but recent research has suggested a potential role. METHODS: We investigated the volumetry of the cerebellum and its subregions in a cohort of 44 patients with genetic FTD (20 MAPT, 7 GRN, and 17 C9orf72 mutation carriers) compared with 18 cognitively normal controls. All groups were matched for age and gender. On volumetric T1-weighted magnetic resonance brain images we used an atlas propagation and label fusion strategy of the Diedrichsen cerebellar atlas to automatically extract subregions including the cerebellar lobules, the vermis and the deep nuclei. RESULTS: The global cerebellar volume was significantly smaller in C9orf72 carriers (mean (SD): 99989 (8939) mm(3)) compared with controls (108136 (7407) mm(3)). However, no significant differences were seen in the MAPT and GRN carriers compared with controls (104191 (6491) mm(3) and 107883 (6205) mm(3) respectively). Investigating the individual subregions, C9orf72 carriers had a significantly lower volume than controls in lobule VIIa-Crus I (15% smaller, p < 0.0005), whilst MAPT mutation carriers had a significantly lower vermal volume (10% smaller, p = 0.001) than controls. All cerebellar subregion volumes were preserved in GRN carriers compared with controls. CONCLUSION: There appears to be a differential pattern of cerebellar atrophy in the major genetic forms of FTD, being relatively spared in GRN, localized to the lobule VIIa-Crus I in the superior-posterior region of the cerebellum in C9orf72, the area connected via the thalamus to the prefrontal cortex and involved in cognitive function, and localized to the vermis in MAPT, the 'limbic cerebellum' involved in emotional processing

Molecular nexopathies: a new paradigm of neurodegenerative disease.

Neural networks provide candidate substrates for the spread of proteinopathies causing neurodegeneration, and emerging data suggest that macroscopic signatures of network disintegration differentiate diseases. However, how do protein abnormalities produce network signatures? The answer may lie with 'molecular nexopathies': specific, coherent conjunctions of pathogenic protein and intrinsic network characteristics that define network signatures of neurodegenerative pathologies. Key features of the paradigm that we propose here include differential intrinsic network vulnerability to propagating protein abnormalities, in part reflecting developmental structural and functional factors; differential vulnerability of neural connection types (e.g., clustered versus distributed connections) to particular pathogenic proteins; and differential impact of molecular effects (e.g., toxic-gain-of-function versus loss-of-function) on gradients of network damage. The paradigm has implications for understanding and predicting neurodegenerative disease biology

Data-Driven Sequence of Changes to Anatomical Brain Connectivity in Sporadic Alzheimer's Disease

Model-based investigations of transneuronal spreading mechanisms in neurodegenerative diseases relate the pattern of pathology severity to the brain’s connectivity matrix, which reveals information about how pathology propagates through the connectivity network. Such network models typically use networks based on functional or structural connectivity in young and healthy individuals, and only end-stage patterns of pathology, thereby ignoring/excluding the effects of normal aging and disease progression. Here, we examine the sequence of changes in the elderly brain’s anatomical connectivity over the course of a neurodegenerative disease. We do this in a data-driven manner that is not dependent upon clinical disease stage, by using event-based disease progression modeling. Using data from the Alzheimer’s Disease Neuroimaging Initiative dataset, we sequence the progressive decline of anatomical connectivity, as quantified by graph-theory metrics, in the Alzheimer’s disease brain. Ours is the first single model to contribute to understanding all three of the nature, the location, and the sequence of changes to anatomical connectivity in the human brain due to Alzheimer’s disease. Our experimental results reveal new insights into Alzheimer’s disease: that degeneration of anatomical connectivity in the brain may be a viable, even early, biomarker and should be considered when studying such neurodegenerative diseases