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Vasilkovska T, Salajeghe S, Vanreusel V, Van Audekerke J, Verschuuren M, Hirschler L, Warnking J, Pintelon I, Pustina D, Cachope R, Mrzljak L, Muñoz-Sanjuan I, Barbier EL, De Vos WH, Van der Linden A, Verhoye M. Longitudinal alterations in brain perfusion and vascular reactivity in the zQ175DN mouse model of Huntington's disease. J Biomed Sci 2024; 31:37. [PMID: 38627751 PMCID: PMC11022401 DOI: 10.1186/s12929-024-01028-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Accepted: 04/08/2024] [Indexed: 04/19/2024] Open
Abstract
BACKGROUND Huntington's disease (HD) is marked by a CAG-repeat expansion in the huntingtin gene that causes neuronal dysfunction and loss, affecting mainly the striatum and the cortex. Alterations in the neurovascular coupling system have been shown to lead to dysregulated energy supply to brain regions in several neurological diseases, including HD, which could potentially trigger the process of neurodegeneration. In particular, it has been observed in cross-sectional human HD studies that vascular alterations are associated to impaired cerebral blood flow (CBF). To assess whether whole-brain changes in CBF are present and follow a pattern of progression, we investigated both resting-state brain perfusion and vascular reactivity longitudinally in the zQ175DN mouse model of HD. METHODS Using pseudo-continuous arterial spin labelling (pCASL) MRI in the zQ175DN model of HD and age-matched wild-type (WT) mice, we assessed whole-brain, resting-state perfusion at 3, 6 and 9 and 13 months of age, and assessed hypercapnia-induced cerebrovascular reactivity (CVR), at 4.5, 6, 9 and 15 months of age. RESULTS We found increased perfusion in cortical regions of zQ175DN HET mice at 3 months of age, and a reduction of this anomaly at 6 and 9 months, ages at which behavioural deficits have been reported. On the other hand, under hypercapnia, CBF was reduced in zQ175DN HET mice as compared to the WT: for multiple brain regions at 6 months of age, for only somatosensory and retrosplenial cortices at 9 months of age, and brain-wide by 15 months. CVR impairments in cortical regions, the thalamus and globus pallidus were observed in zQ175DN HET mice at 9 months, with whole brain reactivity diminished at 15 months of age. Interestingly, blood vessel density was increased in the motor cortex at 3 months, while average vessel length was reduced in the lateral portion of the caudate putamen at 6 months of age. CONCLUSION Our findings reveal early cortical resting-state hyperperfusion and impaired CVR at ages that present motor anomalies in this HD model, suggesting that further characterization of brain perfusion alterations in animal models is warranted as a potential therapeutic target in HD.
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Affiliation(s)
- Tamara Vasilkovska
- Bio-Imaging Lab, University of Antwerp, Universiteitsplein 1, 2610 Wilrijk, Antwerp, Belgium.
- µNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium.
| | - Somaie Salajeghe
- Bio-Imaging Lab, University of Antwerp, Universiteitsplein 1, 2610 Wilrijk, Antwerp, Belgium
| | - Verdi Vanreusel
- Bio-Imaging Lab, University of Antwerp, Universiteitsplein 1, 2610 Wilrijk, Antwerp, Belgium
| | - Johan Van Audekerke
- Bio-Imaging Lab, University of Antwerp, Universiteitsplein 1, 2610 Wilrijk, Antwerp, Belgium
- µNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium
| | - Marlies Verschuuren
- µNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium
- Laboratory of Cell Biology and Histology, University of Antwerp, Universiteitsplein 1, 2610 Wilrijk, Antwerp, Belgium
- Antwerp Centre for Advanced Microscopy, Universiteitsplein 1, 2610 Wilrijk, Antwerp, Belgium
| | - Lydiane Hirschler
- C.J. Gorter MRI Center, Leiden University Medical Center, Leiden, the Netherlands
| | - Jan Warnking
- Univ. Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, Grenoble, France
| | - Isabel Pintelon
- µNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium
- Laboratory of Cell Biology and Histology, University of Antwerp, Universiteitsplein 1, 2610 Wilrijk, Antwerp, Belgium
- Antwerp Centre for Advanced Microscopy, Universiteitsplein 1, 2610 Wilrijk, Antwerp, Belgium
| | - Dorian Pustina
- CHDI Management, Inc., the company that manages the scientific activities of CHDI Foundation, Inc, Princeton, NJ, USA
| | - Roger Cachope
- CHDI Management, Inc., the company that manages the scientific activities of CHDI Foundation, Inc, Princeton, NJ, USA
| | - Ladislav Mrzljak
- CHDI Management, Inc., the company that manages the scientific activities of CHDI Foundation, Inc, Princeton, NJ, USA
- Present Address: Takeda Pharmaceuticals, Cambridge, MA, USA
| | - Ignacio Muñoz-Sanjuan
- CHDI Management, Inc., the company that manages the scientific activities of CHDI Foundation, Inc, Princeton, NJ, USA
- Present Address: Cajal Neuroscience Inc, Seattle, WA, USA
| | - Emmanuel L Barbier
- Univ. Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, Grenoble, France
| | - Winnok H De Vos
- µNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium
- Laboratory of Cell Biology and Histology, University of Antwerp, Universiteitsplein 1, 2610 Wilrijk, Antwerp, Belgium
- Antwerp Centre for Advanced Microscopy, Universiteitsplein 1, 2610 Wilrijk, Antwerp, Belgium
| | - Annemie Van der Linden
- Bio-Imaging Lab, University of Antwerp, Universiteitsplein 1, 2610 Wilrijk, Antwerp, Belgium
- µNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium
| | - Marleen Verhoye
- Bio-Imaging Lab, University of Antwerp, Universiteitsplein 1, 2610 Wilrijk, Antwerp, Belgium
- µNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium
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Vidas-Guscic N, van Rijswijk J, Van Audekerke J, Jeurissen B, Nnah I, Tang H, Muñoz-Sanjuan I, Pustina D, Cachope R, Van der Linden A, Bertoglio D, Verhoye M. Diffusion MRI marks progressive alterations in fiber integrity in the zQ175DN mouse model of Huntington's disease. Neurobiol Dis 2024; 193:106438. [PMID: 38365045 DOI: 10.1016/j.nbd.2024.106438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 01/24/2024] [Accepted: 02/13/2024] [Indexed: 02/18/2024] Open
Abstract
Huntington's disease (HD) is a progressive neurodegenerative disease affecting motor and cognitive abilities. Multiple studies have found white matter anomalies in HD-affected humans and animal models of HD. The identification of sensitive white-matter-based biomarkers in HD animal models will be important in understanding disease mechanisms and testing the efficacy of therapeutic interventions. Here we investigated the progression of white matter deficits in the knock-in zQ175DN heterozygous (HET) mouse model of HD at 3, 6 and 11 months of age (M), reflecting different states of phenotypic progression. We compared findings from traditional diffusion tensor imaging (DTI) and advanced fixel-based analysis (FBA) diffusion metrics for their sensitivity in detecting white matter anomalies in the striatum, motor cortex, and segments of the corpus callosum. FBA metrics revealed progressive and widespread reductions of fiber cross-section and fiber density in myelinated bundles of HET mice. The corpus callosum genu was the most affected structure in HET mice at 6 and 11 M based on the DTI and FBA metrics, while the striatum showed the earliest progressive differences starting at 3 M based on the FBA metrics. Overall, FBA metrics detected earlier and more prominent alterations in myelinated fiber bundles compared to the DTI metrics. Luxol fast blue staining showed no loss in myelin density, indicating that diffusion anomalies could not be explained by myelin reduction but diffusion anomalies in HET mice were accompanied by increased levels of neurofilament light chain protein at 11 M. Altogether, our findings reveal progressive alterations in myelinated fiber bundles that can be measured using diffusion MRI, representing a candidate noninvasive imaging biomarker to study phenotype progression and the efficacy of therapeutic interventions in zQ175DN mice. Moreover, our study exposed higher sensitivity of FBA than DTI metrics, suggesting a potential benefit of adopting these advanced metrics in other contexts, including biomarker development in humans.
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Affiliation(s)
- Nicholas Vidas-Guscic
- Bio-Imaging Lab, University of Antwerp, Antwerp, Belgium; μNeuro Center for Excellence, University of Antwerp, Antwerp, Belgium.
| | - Joëlle van Rijswijk
- Bio-Imaging Lab, University of Antwerp, Antwerp, Belgium; μNeuro Center for Excellence, University of Antwerp, Antwerp, Belgium
| | - Johan Van Audekerke
- Bio-Imaging Lab, University of Antwerp, Antwerp, Belgium; μNeuro Center for Excellence, University of Antwerp, Antwerp, Belgium
| | - Ben Jeurissen
- μNeuro Center for Excellence, University of Antwerp, Antwerp, Belgium; Vision Lab, University of Antwerp, Antwerp, Belgium; Lab for Equilibrium Investigations and Aerospace, University of Antwerp, Antwerp, Belgium
| | - Israel Nnah
- Charles River Laboratories, Shrewsbury, MA, United states
| | - Haiying Tang
- CHDI Management, Inc., the company that manages the scientific activities of CHDI Foundation, Inc., Princeton, NJ, United States
| | - Ignacio Muñoz-Sanjuan
- CHDI Management, Inc., the company that manages the scientific activities of CHDI Foundation, Inc., Princeton, NJ, United States
| | - Dorian Pustina
- CHDI Management, Inc., the company that manages the scientific activities of CHDI Foundation, Inc., Princeton, NJ, United States
| | - Roger Cachope
- CHDI Management, Inc., the company that manages the scientific activities of CHDI Foundation, Inc., Princeton, NJ, United States
| | - Annemie Van der Linden
- Bio-Imaging Lab, University of Antwerp, Antwerp, Belgium; μNeuro Center for Excellence, University of Antwerp, Antwerp, Belgium
| | - Daniele Bertoglio
- Bio-Imaging Lab, University of Antwerp, Antwerp, Belgium; μNeuro Center for Excellence, University of Antwerp, Antwerp, Belgium
| | - Marleen Verhoye
- Bio-Imaging Lab, University of Antwerp, Antwerp, Belgium; μNeuro Center for Excellence, University of Antwerp, Antwerp, Belgium
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Wijeratne PA, Eshaghi A, Scotton WJ, Kohli M, Aksman L, Oxtoby NP, Pustina D, Warner JH, Paulsen JS, Scahill RI, Sampaio C, Tabrizi SJ, Alexander DC. The temporal event-based model: Learning event timelines in progressive diseases. Imaging Neurosci (Camb) 2023; 1:1-19. [PMID: 37719837 PMCID: PMC10503481 DOI: 10.1162/imag_a_00010] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 07/15/2023] [Indexed: 09/19/2023]
Abstract
Timelines of events, such as symptom appearance or a change in biomarker value, provide powerful signatures that characterise progressive diseases. Understanding and predicting the timing of events is important for clinical trials targeting individuals early in the disease course when putative treatments are likely to have the strongest effect. However, previous models of disease progression cannot estimate the time between events and provide only an ordering in which they change. Here, we introduce the temporal event-based model (TEBM), a new probabilistic model for inferring timelines of biomarker events from sparse and irregularly sampled datasets. We demonstrate the power of the TEBM in two neurodegenerative conditions: Alzheimer's disease (AD) and Huntington's disease (HD). In both diseases, the TEBM not only recapitulates current understanding of event orderings but also provides unique new ranges of timescales between consecutive events. We reproduce and validate these findings using external datasets in both diseases. We also demonstrate that the TEBM improves over current models; provides unique stratification capabilities; and enriches simulated clinical trials to achieve a power of 80 % with less than half the cohort size compared with random selection. The application of the TEBM naturally extends to a wide range of progressive conditions.
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Affiliation(s)
- Peter A. Wijeratne
- UCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
- Department of Informatics, University of Sussex, Brighton, United Kingdom
| | - Arman Eshaghi
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, University College London, London, United Kingdom
| | - William J. Scotton
- Dementia Research Centre, Department of Neurodegenerative Disease, University College London, London, United Kingdom
| | - Maitrei Kohli
- UCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
| | - Leon Aksman
- Keck School of Medicine, University of Southern California, Los Angeles, California, United States
| | - Neil P. Oxtoby
- UCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
| | - Dorian Pustina
- CHDI Management/CHDI Foundation, Princeton, New Jersey, United States
| | - John H. Warner
- CHDI Management/CHDI Foundation, Princeton, New Jersey, United States
| | - Jane S. Paulsen
- Departments of Neurology and Psychiatry, Carver College of Medicine, University of Iowa, Iowa City, Iowa, United States
| | - Rachael I. Scahill
- Huntington’s Disease Centre, Department of Neurodegenerative Disease, University College London, Queen Square, London, United Kingdom
| | - Cristina Sampaio
- CHDI Management/CHDI Foundation, Princeton, New Jersey, United States
| | - Sarah J. Tabrizi
- Huntington’s Disease Centre, Department of Neurodegenerative Disease, University College London, Queen Square, London, United Kingdom
| | - Daniel C. Alexander
- UCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
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Adhikari MH, Vasilkovska T, Cachope R, Tang H, Liu L, Keliris GA, Munoz-Sanjuan I, Pustina D, Van der Linden A, Verhoye M. Longitudinal investigation of changes in resting-state co-activation patterns and their predictive ability in the zQ175 DN mouse model of Huntington's disease. Sci Rep 2023; 13:10194. [PMID: 37353500 PMCID: PMC10290061 DOI: 10.1038/s41598-023-36812-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 06/10/2023] [Indexed: 06/25/2023] Open
Abstract
Huntington's disease (HD) is a neurodegenerative disorder caused by expanded (≥ 40) glutamine-encoding CAG repeats in the huntingtin gene, which leads to dysfunction and death of predominantly striatal and cortical neurons. While the genetic profile and clinical signs and symptoms of the disease are better known, changes in the functional architecture of the brain, especially before the clinical expression becomes apparent, are not fully and consistently characterized. In this study, we sought to uncover functional changes in the brain in the heterozygous (HET) zQ175 delta-neo (DN) mouse model at 3, 6, and 10 months of age, using resting-state functional magnetic resonance imaging (RS-fMRI). This mouse model shows molecular, cellular and circuitry alterations that worsen through age. Motor function disturbances are manifested in this model at 6 and 10 months of age. Specifically, we investigated, longitudinally, changes in co-activation patterns (CAPs) that are the transient states of brain activity constituting the resting-state networks (RSNs). Most robust changes in the temporal properties of CAPs occurred at the 10-months time point; the durations of two anti-correlated CAPs, characterized by simultaneous co-activation of default-mode like network (DMLN) and co-deactivation of lateral-cortical network (LCN) and vice-versa, were reduced in the zQ175 DN HET animals compared to the wild-type mice. Changes in the spatial properties, measured in terms of activation levels of different brain regions, during CAPs were found at all three ages and became progressively more pronounced at 6-, and 10 months of age. We then assessed the cross-validated predictive power of CAP metrics to distinguish HET animals from controls. Spatial properties of CAPs performed significantly better than the chance level at all three ages with 80% classification accuracy at 6 and 10 months of age.
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Affiliation(s)
- Mohit H Adhikari
- Bio-Imaging Lab, University of Antwerp, Antwerp, Belgium.
- µNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium.
| | - Tamara Vasilkovska
- Bio-Imaging Lab, University of Antwerp, Antwerp, Belgium
- µNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium
| | - Roger Cachope
- CHDI Management for CHDI Foundation, Princeton, NJ, USA
| | - Haiying Tang
- CHDI Management for CHDI Foundation, Princeton, NJ, USA
| | - Longbin Liu
- CHDI Management for CHDI Foundation, Princeton, NJ, USA
| | - Georgios A Keliris
- Institute of Computer Science, Foundation for Research and Technology - Hellas, Heraklion, Crete, Greece
| | | | | | - Annemie Van der Linden
- Bio-Imaging Lab, University of Antwerp, Antwerp, Belgium
- µNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium
| | - Marleen Verhoye
- Bio-Imaging Lab, University of Antwerp, Antwerp, Belgium
- µNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium
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Hinds W, Modi S, Ankeeta A, Sperling MR, Pustina D, Tracy JI. Pre-surgical features of intrinsic brain networks predict single and joint epilepsy surgery outcomes. Neuroimage Clin 2023; 38:103387. [PMID: 37023491 PMCID: PMC10122017 DOI: 10.1016/j.nicl.2023.103387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 03/02/2023] [Accepted: 03/24/2023] [Indexed: 03/31/2023]
Abstract
Despite the effectiveness of surgical interventions for the treatment of intractable focal temporal lobe epilepsy (TLE), the substrates that support good outcomes are poorly understood. While algorithms have been developed for the prediction of either seizure or cognitive/psychiatric outcomes alone, no study has reported on the functional and structural architecture that supports joint outcomes. We measured key aspects of pre-surgical whole brain functional/structural network architecture and evaluated their ability to predict post-operative seizure control in combination with cognitive/psychiatric outcomes. Pre-surgically, we identified the intrinsic connectivity networks (ICNs) unique to each person through independent component analysis (ICA), and computed: (1) the spatial-temporal match between each person's ICA components and established, canonical ICNs, (2) the connectivity strength within each identified person-specific ICN, (3) the gray matter (GM) volume underlying the person-specific ICNs, and (4) the amount of variance not explained by the canonical ICNs for each person. Post-surgical seizure control and reliable change indices of change (for language [naming, phonemic fluency], verbal episodic memory, and depression) served as binary outcome responses in random forest (RF) models. The above functional and structural measures served as input predictors. Our empirically derived ICN-based measures customized to the individual showed that good joint seizure and cognitive/psychiatric outcomes depended upon higher levels of brain reserve (GM volume) in specific networks. In contrast, singular outcomes relied on systematic, idiosyncratic variance in the case of seizure control, and the weakened pre-surgical presence of functional ICNs that encompassed the ictal temporal lobe in the case of cognitive/psychiatric outcomes. Our data made clear that the ICNs differed in their propensity to provide reserve for adaptive outcomes, with some providing structural (brain), and others functional (cognitive) reserve. Our customized methodology demonstrated that when substantial unique, patient-specific ICNs are present prior to surgery there is a reliable association with poor post-surgical seizure control. These ICNs are idiosyncratic in that they did not match the canonical, normative ICNs and, therefore, could not be defined functionally, with their location likely varying by patient. This important finding suggested the level of highly individualized ICN's in the epileptic brain may signal the emergence of epileptogenic activity after surgery.
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Affiliation(s)
- Walter Hinds
- Thomas Jefferson University, Department of Neurology, and Vicky and Jack Farber Institute for Neuroscience, USA
| | - Shilpi Modi
- Thomas Jefferson University, Department of Neurology, and Vicky and Jack Farber Institute for Neuroscience, USA
| | - Ankeeta Ankeeta
- Thomas Jefferson University, Department of Neurology, and Vicky and Jack Farber Institute for Neuroscience, USA
| | - Michael R Sperling
- Thomas Jefferson University, Department of Neurology, and Vicky and Jack Farber Institute for Neuroscience, USA
| | | | - Joseph I Tracy
- Thomas Jefferson University, Department of Neurology, and Vicky and Jack Farber Institute for Neuroscience, USA.
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Vasilkovska T, Adhikari M, Van Audekerke J, Salajeghe S, Pustina D, Cachope R, Tang H, Liu L, Munoz-Sanjuan I, Van der Linden A, Verhoye M. Resting-state fMRI reveals longitudinal alterations in brain network connectivity in the zQ175DN mouse model of Huntington's disease. Neurobiol Dis 2023; 181:106095. [PMID: 36963694 DOI: 10.1016/j.nbd.2023.106095] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Revised: 03/13/2023] [Accepted: 03/20/2023] [Indexed: 03/26/2023] Open
Abstract
Huntington's disease is an autosomal, dominantly inherited neurodegenerative disease caused by an expansion of the CAG repeats in exon 1 of the huntingtin gene. Neuronal degeneration and dysfunction that precedes regional atrophy result in the impairment of striatal and cortical circuits that affect the brain's large-scale network functionality. However, the evolution of these disease-driven, large-scale connectivity alterations is still poorly understood. Here we used resting-state fMRI to investigate functional connectivity changes in a mouse model of Huntington's disease in several relevant brain networks and how they are affected at different ages that follow a disease-like phenotypic progression. Towards this, we used the heterozygous (HET) form of the zQ175DN Huntington's disease mouse model that recapitulates aspects of human disease pathology. Seed- and Region-based analyses were performed at different ages, on 3-, 6-, 10-, and 12-month-old HET and age-matched wild-type mice. Our results demonstrate decreased connectivity starting at 6 months of age, most prominently in regions such as the retrosplenial and cingulate cortices, pertaining to the default mode-like network and auditory and visual cortices, part of the associative cortical network. At 12 months, we observe a shift towards decreased connectivity in regions such as the somatosensory cortices, pertaining to the lateral cortical network, and the caudate putamen, a constituent of the subcortical network. Moreover, we assessed the impact of distinct Huntington's Disease-like pathology of the zQ175DN HET mice on age-dependent connectivity between different brain regions and networks where we demonstrate that connectivity strength follows a nonlinear, inverted U-shape pattern, a well-known phenomenon of development and normal aging. Conversely, the neuropathologically driven alteration of connectivity, especially in the default mode and associative cortical networks, showed diminished age-dependent evolution of functional connectivity. These findings reveal that in this Huntington's disease model, altered connectivity starts with cortical network aberrations which precede striatal connectivity changes, that appear only at a later age. Taken together, these results suggest that the age-dependent cortical network dysfunction seen in rodents could represent a relevant pathological process in Huntington's disease progression.
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Affiliation(s)
- Tamara Vasilkovska
- Bio-Imaging Lab, University of Antwerp, Universiteitsplein 1, 2610 Wilrijk, Antwerp, Belgium; μNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium.
| | - Mohit Adhikari
- Bio-Imaging Lab, University of Antwerp, Universiteitsplein 1, 2610 Wilrijk, Antwerp, Belgium; μNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium
| | - Johan Van Audekerke
- Bio-Imaging Lab, University of Antwerp, Universiteitsplein 1, 2610 Wilrijk, Antwerp, Belgium; μNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium
| | - Somaie Salajeghe
- Bio-Imaging Lab, University of Antwerp, Universiteitsplein 1, 2610 Wilrijk, Antwerp, Belgium
| | | | | | - Haiying Tang
- CHDI Management/CHDI Foundation, Princeton, NJ, USA
| | - Longbin Liu
- CHDI Management/CHDI Foundation, Princeton, NJ, USA
| | | | - Annemie Van der Linden
- Bio-Imaging Lab, University of Antwerp, Universiteitsplein 1, 2610 Wilrijk, Antwerp, Belgium; μNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium
| | - Marleen Verhoye
- Bio-Imaging Lab, University of Antwerp, Universiteitsplein 1, 2610 Wilrijk, Antwerp, Belgium; μNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium
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Castro E, Polosecki P, Pustina D, Wood A, Sampaio C, Cecchi GA. Predictive Modeling of Huntington's Disease Unfolds Thalamic and Caudate Atrophy Dissociation. Mov Disord 2022; 37:2407-2416. [PMID: 36173150 DOI: 10.1002/mds.29219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 06/16/2022] [Accepted: 07/28/2022] [Indexed: 01/18/2023] Open
Abstract
BACKGROUND Atrophy in the striatum is a hallmark of Huntington's disease (HD), including the period before clinical motor diagnosis (before-CMD), but it extends to other subcortical structures. The study of the covariation of these structures could improve the detection of disease-related longitudinal progression before-CMD, provide mechanistic insights of the disease, and potentially be used to obtain accurate prospective estimates of atrophy before-CMD and early after-CMD. METHODS We analyzed data from 337 before-CMD individuals, 236 healthy control subjects, and 95 early after-CMD individuals from three studies, and we used nine subcortical regions volumes in two analyses. First, we discriminated before-CMD from healthy control trajectories by integrating volume changes from these regions. Second, we estimated prospective atrophy before-CMD and early after-CMD by considering the influence of a region's present volume over the future volume of another one. RESULTS Before-CMD progression was robustly detected across studies. Indeed, detection of before-CMD progression improved when multiple structures were integrated, as opposed to analyzing the striatum alone, likely because of the reduced partial correlation between caudate and thalamic volume change before-CMD. Our multivariate atrophy prediction model found a thalamus-caudate association that is consistent with this pattern, which yields an improved caudate atrophy prediction in early after-CMD. CONCLUSIONS This study is the first attempt to validate before-CMD multivariate subcortical change detection across studies and to do multivariate prospective atrophy prediction in HD. These models achieve improved performance by detecting a dissociation between caudate and thalamic atrophy trajectories, and they provide a possible mechanistic understanding of the dynamics of HD. © 2022 International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Eduardo Castro
- Digital Health, IBM T.J. Watson Research Center, Yorktown Heights, New York, USA
| | - Pablo Polosecki
- Digital Health, IBM T.J. Watson Research Center, Yorktown Heights, New York, USA
| | - Dorian Pustina
- CHDI Management/CHDI Foundation, Princeton, New Jersey, USA
| | - Andrew Wood
- CHDI Management/CHDI Foundation, Princeton, New Jersey, USA
| | | | - Guillermo A Cecchi
- Digital Health, IBM T.J. Watson Research Center, Yorktown Heights, New York, USA
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Medaglia JD, Erickson BA, Pustina D, Kelkar AS, DeMarco AT, Dickens JV, Turkeltaub PE. Simulated Attack Reveals How Lesions Affect Network Properties in Poststroke Aphasia. J Neurosci 2022; 42:4913-4926. [PMID: 35545436 PMCID: PMC9188386 DOI: 10.1523/jneurosci.1163-21.2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2021] [Revised: 02/17/2022] [Accepted: 02/18/2022] [Indexed: 11/21/2022] Open
Abstract
Aphasia is a prevalent cognitive syndrome caused by stroke. The rarity of premorbid imaging and heterogeneity of lesion obscures the links between the local effects of the lesion, global anatomic network organization, and aphasia symptoms. We applied a simulated attack approach in humans to examine the effects of 39 stroke lesions (16 females) on anatomic network topology by simulating their effects in a control sample of 36 healthy (15 females) brain networks. We focused on measures of global network organization thought to support overall brain function and resilience in the whole brain and within the left hemisphere. After removing lesion volume from the network topology measures and behavioral scores [the Western Aphasia Battery Aphasia Quotient (WAB-AQ), four behavioral factor scores obtained from a neuropsychological battery, and a factor sum], we compared the behavioral variance accounted for by simulated poststroke connectomes to that observed in the randomly permuted data. Global measures of anatomic network topology in the whole brain and left hemisphere accounted for 10% variance or more of the WAB-AQ and the lexical factor score beyond lesion volume and null permutations. Streamline networks provided more reliable point estimates than FA networks. Edge weights and network efficiency were weighted most highly in predicting the WAB-AQ for FA networks. Overall, our results suggest that global network measures provide modest statistical value beyond lesion volume when predicting overall aphasia severity, but less value in predicting specific behaviors. Variability in estimates could be induced by premorbid ability, deafferentation and diaschisis, and neuroplasticity following stroke.SIGNIFICANCE STATEMENT Poststroke, the remaining neuroanatomy maintains cognition and supports recovery. However, studies often use small, cross-sectional samples that cannot fully model the interactions between lesions and other variables that affect networks in stroke. Alternate methods are required to account for these effects. "Simulated attack" models are computational approaches that apply virtual damage to the brain and measure their putative consequences. Using a simulated attack model, we estimated how simulated damage to anatomic networks could account for language performance. Overall, our results reveal that global network measures can provide modest statistical value predicting overall aphasia severity, but less value in predicting specific behaviors. These findings suggest that more theoretically precise network models could be necessary to robustly predict individual outcomes in aphasia.
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Affiliation(s)
- John D Medaglia
- Department of Psychology, Drexel University, Philadelphia, Pennsylvania 19104
- Department of Neurology, Drexel University, Philadelphia, Pennsylvania 19104
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - Brian A Erickson
- Department of Psychology, Drexel University, Philadelphia, Pennsylvania 19104
| | - Dorian Pustina
- Cure Huntingdon's Disease Initiative (CHDI) Foundation, Princeton, New Jersey 08540
| | - Apoorva S Kelkar
- Department of Psychology, Drexel University, Philadelphia, Pennsylvania 19104
| | - Andrew T DeMarco
- Department of Neurology, Georgetown University, Washington, DC 20007
| | - J Vivian Dickens
- Department of Neurology, Georgetown University, Washington, DC 20007
| | - Peter E Turkeltaub
- Department of Neurology, Georgetown University, Washington, DC 20007
- MedStar National Rehabilitation Hospital, Washington, DC 20007
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9
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Kinnunen KM, Mullin AP, Pustina D, Turner EC, Burton J, Gordon MF, Scahill RI, Gantman EC, Noble S, Romero K, Georgiou-Karistianis N, Schwarz AJ. Recommendations to Optimize the Use of Volumetric MRI in Huntington's Disease Clinical Trials. Front Neurol 2021; 12:712565. [PMID: 34744964 PMCID: PMC8569234 DOI: 10.3389/fneur.2021.712565] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 09/21/2021] [Indexed: 12/12/2022] Open
Abstract
Volumetric magnetic resonance imaging (vMRI) has been widely studied in Huntington's disease (HD) and is commonly used to assess treatment effects on brain atrophy in interventional trials. Global and regional trajectories of brain atrophy in HD, with early involvement of striatal regions, are becoming increasingly understood. However, there remains heterogeneity in the methods used and a lack of widely-accessible multisite, longitudinal, normative datasets in HD. Consensus for standardized practices for data acquisition, analysis, sharing, and reporting will strengthen the interpretation of vMRI results and facilitate their adoption as part of a pathobiological disease staging system. The Huntington's Disease Regulatory Science Consortium (HD-RSC) currently comprises 37 member organizations and is dedicated to building a regulatory science strategy to expedite the approval of HD therapeutics. Here, we propose four recommendations to address vMRI standardization in HD research: (1) a checklist of standardized practices for the use of vMRI in clinical research and for reporting results; (2) targeted research projects to evaluate advanced vMRI methodologies in HD; (3) the definition of standard MRI-based anatomical boundaries for key brain structures in HD, plus the creation of a standard reference dataset to benchmark vMRI data analysis methods; and (4) broad access to raw images and derived data from both observational studies and interventional trials, coded to protect participant identity. In concert, these recommendations will enable a better understanding of disease progression and increase confidence in the use of vMRI for drug development.
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Affiliation(s)
| | - Ariana P Mullin
- Critical Path Institute, Tucson, AZ, United States.,Wave Life Sciences, Ltd., Cambridge, MA, United States
| | - Dorian Pustina
- CHDI Management/CHDI Foundation, Princeton, NJ, United States
| | | | | | - Mark F Gordon
- Teva Pharmaceuticals, West Chester, PA, United States
| | - Rachael I Scahill
- Huntington's Disease Research Centre, UCL Institute of Neurology, London, United Kingdom
| | - Emily C Gantman
- CHDI Management/CHDI Foundation, Princeton, NJ, United States
| | - Simon Noble
- CHDI Management/CHDI Foundation, Princeton, NJ, United States
| | - Klaus Romero
- Critical Path Institute, Tucson, AZ, United States
| | - Nellie Georgiou-Karistianis
- School of Psychological Sciences and Turner Institute for Brain and Mental Health, Monash University, Melbourne, VIC, Australia
| | - Adam J Schwarz
- Takeda Pharmaceuticals, Ltd., Cambridge, MA, United States
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10
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Kinnunen KM, Schwarz AJ, Turner EC, Pustina D, Gantman EC, Gordon MF, Joules R, Mullin AP, Scahill RI, Georgiou-Karistianis N. Volumetric MRI-Based Biomarkers in Huntington's Disease: An Evidentiary Review. Front Neurol 2021; 12:712555. [PMID: 34621236 PMCID: PMC8490802 DOI: 10.3389/fneur.2021.712555] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 08/10/2021] [Indexed: 01/02/2023] Open
Abstract
Huntington's disease (HD) is an autosomal-dominant inherited neurodegenerative disorder that is caused by expansion of a CAG-repeat tract in the huntingtin gene and characterized by motor impairment, cognitive decline, and neuropsychiatric disturbances. Neuropathological studies show that disease progression follows a characteristic pattern of brain atrophy, beginning in the basal ganglia structures. The HD Regulatory Science Consortium (HD-RSC) brings together diverse stakeholders in the HD community—biopharmaceutical industry, academia, nonprofit, and patient advocacy organizations—to define and address regulatory needs to accelerate HD therapeutic development. Here, the Biomarker Working Group of the HD-RSC summarizes the cross-sectional evidence indicating that regional brain volumes, as measured by volumetric magnetic resonance imaging, are reduced in HD and are correlated with disease characteristics. We also evaluate the relationship between imaging measures and clinical change, their longitudinal change characteristics, and within-individual longitudinal associations of imaging with disease progression. This analysis will be valuable in assessing pharmacodynamics in clinical trials and supporting clinical outcome assessments to evaluate treatment effects on neurodegeneration.
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Affiliation(s)
| | - Adam J Schwarz
- Takeda Pharmaceuticals, Ltd., Cambridge, MA, United States
| | | | - Dorian Pustina
- CHDI Management/CHDI Foundation, Princeton, NJ, United States
| | - Emily C Gantman
- CHDI Management/CHDI Foundation, Princeton, NJ, United States
| | - Mark F Gordon
- Teva Pharmaceuticals, West Chester, PA, United States
| | | | - Ariana P Mullin
- Critical Path Institute, Tucson, AZ, United States.,Wave Life Sciences, Ltd., Cambridge, MA, United States
| | - Rachael I Scahill
- Huntington's Disease Research Centre, UCL Institute of Neurology, London, United Kingdom
| | - Nellie Georgiou-Karistianis
- School of Psychological Sciences and Turner Institute for Brain and Mental Health, Monash University, Melbourne, VIC, Australia
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11
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Abeyasinghe PM, Long JD, Razi A, Pustina D, Paulsen JS, Tabrizi SJ, Poudel GR, Georgiou-Karistianis N. Tracking Huntington's Disease Progression Using Motor, Functional, Cognitive, and Imaging Markers. Mov Disord 2021; 36:2282-2292. [PMID: 34014005 DOI: 10.1002/mds.28650] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Revised: 04/25/2021] [Accepted: 04/27/2021] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Potential therapeutic targets and clinical trials for Huntington's disease have grown immensely in the last decade. However, to improve clinical trial outcomes, there is a need to better characterize profiles of signs and symptoms across different epochs of the disease to improve selection of participants. OBJECTIVE The objective of the present study was to best distinguish longitudinal trajectories across different Huntington's disease progression groups. METHODS Clinical and morphometric imaging data from 1082 participants across IMAGE-HD, TRACK-HD, and PREDICT-HD studies were combined, with longitudinal times ranging between 1 and 10 years. Participants were classified into 4 groups using CAG and age product. Using multivariate linear mixed modeling, 63 combinations of markers were tested for their sensitivity in differentiating CAG and age product groups. Next, multivariate linear mixed modeling was applied to define the best combination of markers to track progression across individual CAG and age product groups. RESULTS Putamen and caudate volumes, individually and/or combined, were identified as the best variables to both differentiate CAG and age product groups and track progression within them. The model using only caudate volume best described advanced disease progression in the combined data set. Contrary to expectations, combining clinical markers and volumetric measures did not improve tracking longitudinal progression. CONCLUSIONS Monitoring volumetric changes throughout a trial (alongside primary and secondary clinical end points) may provide a more comprehensive understanding of improvements in functional outcomes and help to improve the design of clinical trials. Alternatively, our results suggest that imaging deserves consideration as an end point in clinical trials because of the prospect of greater sensitivity. © 2021 International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Pubu M Abeyasinghe
- School of Psychological Sciences and Turner Institute for Brain and Mental Health, Monash University, Melbourne, Victoria, Australia
| | - Jeffrey D Long
- Department of Psychiatry, Carver Collage of Medicine, The University of Iowa, Iowa City, Iowa, USA.,Department of Biostatistics, College of Public Health, The University of Iowa, Iowa City, Iowa, USA
| | - Adeel Razi
- School of Psychological Sciences and Turner Institute for Brain and Mental Health, Monash University, Melbourne, Victoria, Australia.,Monash Biomedical Imaging, Monash University, Clayton, Victoria, Australia.,Wellcome Centre for Human Neuroimaging, UCL, London, United Kingdom
| | - Dorian Pustina
- CHDI Management/CHDI Foundation, Princeton, New Jersey, USA
| | - Jane S Paulsen
- Department of Neurology, University of Wisconsin, Madison, Wisconsin, USA
| | - Sarah J Tabrizi
- UCL Department of Neurodegenerative Disease and Huntington's Disease Centre, UCL Queen Square Institute of Neurology, Dementia Research Institute at UCL, London, United Kingdom
| | - Govinda R Poudel
- Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne, Victoria, Australia
| | - Nellie Georgiou-Karistianis
- School of Psychological Sciences and Turner Institute for Brain and Mental Health, Monash University, Melbourne, Victoria, Australia
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12
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Polosecki P, Castro E, Rish I, Pustina D, Warner JH, Wood A, Sampaio C, Cecchi GA. Resting-state connectivity stratifies premanifest Huntington's disease by longitudinal cognitive decline rate. Sci Rep 2020; 10:1252. [PMID: 31988371 PMCID: PMC6985137 DOI: 10.1038/s41598-020-58074-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2019] [Accepted: 01/10/2020] [Indexed: 11/17/2022] Open
Abstract
Patient stratification is critical for the sensitivity of clinical trials at early stages of neurodegenerative disorders. In Huntington’s disease (HD), genetic tests make cognitive, motor and brain imaging measurements possible before symptom manifestation (pre-HD). We evaluated pre-HD stratification models based on single visit resting-state functional MRI (rs-fMRI) data that assess observed longitudinal motor and cognitive change rates from the multisite Track-On HD cohort (74 pre-HD, 79 control participants). We computed longitudinal performance change on 10 tasks (including visits from the preceding TRACK-HD study when available), as well as functional connectivity density (FCD) maps in single rs-fMRI visits, which showed high test-retest reliability. We assigned pre-HD subjects to subgroups of fast, intermediate, and slow change along single tasks or combinations of them, correcting for expectations based on aging; and trained FCD-based classifiers to distinguish fast- from slow-progressing individuals. For robustness, models were validated across imaging sites. Stratification models distinguished fast- from slow-changing participants and provided continuous assessments of decline applicable to the whole pre-HD population, relying on previously-neglected white matter functional signals. These results suggest novel correlates of early deterioration and a robust stratification strategy where a single MRI measurement provides an estimate of multiple ongoing longitudinal changes.
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Affiliation(s)
- Pablo Polosecki
- IBM T.J. Watson Research Center, Yorktown Heights, Yorktown, NY, USA.
| | - Eduardo Castro
- IBM T.J. Watson Research Center, Yorktown Heights, Yorktown, NY, USA
| | - Irina Rish
- IBM T.J. Watson Research Center, Yorktown Heights, Yorktown, NY, USA
| | | | | | - Andrew Wood
- CHDI Management/CHDI Foundation, Princeton, NJ, USA
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13
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Mirman D, Landrigan JF, Kokolis S, Verillo S, Ferrara C, Pustina D. Corrections for multiple comparisons in voxel-based lesion-symptom mapping. Neuropsychologia 2018; 115:112-123. [PMID: 28847712 PMCID: PMC5826816 DOI: 10.1016/j.neuropsychologia.2017.08.025] [Citation(s) in RCA: 57] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2017] [Revised: 08/18/2017] [Accepted: 08/22/2017] [Indexed: 10/19/2022]
Abstract
Voxel-based lesion-symptom mapping (VLSM) is an important method for basic and translational human neuroscience research. VLSM leverages modern neuroimaging analysis techniques to build on the classic approach of examining the relationship between location of brain damage and cognitive deficits. Testing an association between deficit severity and lesion status in each voxel involves very many individual tests and requires statistical correction for multiple comparisons. Several strategies have been adapted from analysis of functional neuroimaging data, though VLSM faces a more difficult trade-off between avoiding false positives and statistical power (missing true effects). We used simulated and real deficit scores from a sample of approximately 100 individuals with left hemisphere stroke to evaluate two such permutation-based approaches. Using permutation to set a minimum cluster size identified a region that systematically extended well beyond the true region, making it ill-suited to identifying brain-behavior relationships. In contrast, generalizing the standard permutation-based family-wise error correction approach provided a principled way to balance false positives and false negatives. Comparison with the widely-used parametric false discovery rate (FDR) correction showed that FDR produces anti-conservative results at smaller sample sizes (N = 30-60). An implementation of the continuous permutation-based FWER correction method described here is included in the lesymap package for lesion-symptom mapping (https://dorianps.github.io/LESYMAP/).
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Affiliation(s)
- Daniel Mirman
- University of Alabama at Birmingham, Birmingham, AL, USA.
| | | | | | | | - Casey Ferrara
- Moss Rehabilitation Research Institute, Elkins Park, PA, USA
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14
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Castro E, Polosecki P, Rish I, Pustina D, Warner JH, Wood A, Sampaio C, Cecchi GA. Baseline multimodal information predicts future motor impairment in premanifest Huntington's disease. Neuroimage Clin 2018; 19:443-453. [PMID: 29984153 PMCID: PMC6029560 DOI: 10.1016/j.nicl.2018.05.008] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/21/2017] [Revised: 05/04/2018] [Accepted: 05/08/2018] [Indexed: 01/24/2023]
Abstract
In Huntington's disease (HD), accurate estimates of expected future motor impairments are key for clinical trials. Individual prognosis is only partially explained by genetics. However, studies so far have focused on predicting the time to clinical diagnosis based on fixed impairment levels, as opposed to predicting impairment in time windows comparable to the duration of a clinical trial. Here we evaluate an approach to both detect atrophy patterns associated with early degeneration and provide a prognosis of motor impairment within 3 years, using data from the TRACK-HD study on 80 premanifest HD (pre-HD) individuals and 85 age- and sex-matched healthy controls. We integrate anatomical MRI information from gray matter concentrations (estimated via voxel-based morphometry) together with baseline data from demographic, genetic and motor domains to distinguish individuals at high risk of developing pronounced future motor impairment from those at low risk. We evaluate the ability of models to distinguish between these two groups solely using baseline imaging data, as well as in combination with longitudinal imaging or non-imaging data. Our models show improved performance for motor prognosis through the incorporation of imaging features to non-imaging data, reaching 88% cross-validated accuracy when using baseline non-longitudinal information, and detect informative correlates in the caudate nucleus and the thalamus both for motor prognosis and early atrophy detection. These results show the plausibility of using baseline imaging and basic demographic/genetic measures for early detection of individuals at high risk of severe future motor impairment in relatively short timeframes. Detection of pre-HD subjects at high risk of impairment is key for clinical trials. Prognostic models of motor impairment can aid the detection of this population. Genetics only partially explains disease progression (need for other correlates). We achieve improved prognosis with baseline imaging, demographics and motor data.
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Affiliation(s)
- Eduardo Castro
- IBM T.J. Watson Research Center, Yorktown Heights, NY, USA.
| | | | - Irina Rish
- IBM T.J. Watson Research Center, Yorktown Heights, NY, USA
| | | | | | - Andrew Wood
- CHDI Management/CHDI Foundation, Princeton, NJ, USA
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15
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Pustina D, Coslett HB, Ungar L, Faseyitan OK, Medaglia JD, Avants B, Schwartz MF. Enhanced estimations of post-stroke aphasia severity using stacked multimodal predictions. Hum Brain Mapp 2017; 38:5603-5615. [PMID: 28782862 DOI: 10.1002/hbm.23752] [Citation(s) in RCA: 43] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2017] [Revised: 07/13/2017] [Accepted: 07/23/2017] [Indexed: 01/03/2023] Open
Abstract
The severity of post-stroke aphasia and the potential for recovery are highly variable and difficult to predict. Evidence suggests that optimal estimation of aphasia severity requires the integration of multiple neuroimaging modalities and the adoption of new methods that can detect multivariate brain-behavior relationships. We created and tested a multimodal framework that relies on three information sources (lesion maps, structural connectivity, and functional connectivity) to create an array of unimodal predictions which are then fed into a final model that creates "stacked multimodal predictions" (STAMP). Crossvalidated predictions of four aphasia scores (picture naming, sentence repetition, sentence comprehension, and overall aphasia severity) were obtained from 53 left hemispheric chronic stroke patients (age: 57.1 ± 12.3 yrs, post-stroke interval: 20 months, 25 female). Results showed accurate predictions for all four aphasia scores (correlation true vs. predicted: r = 0.79-0.88). The accuracy was slightly smaller but yet significant (r = 0.66) in a full split crossvalidation with each patient considered as new. Critically, multimodal predictions produced more accurate results that any single modality alone. Topological maps of the brain regions involved in the prediction were recovered and compared with traditional voxel-based lesion-to-symptom maps, revealing high spatial congruency. These results suggest that neuroimaging modalities carry complementary information potentially useful for the prediction of aphasia scores. More broadly, this study shows that the translation of neuroimaging findings into clinically useful tools calls for a shift in perspective from unimodal to multimodal neuroimaging, from univariate to multivariate methods, from linear to nonlinear models, and, conceptually, from inferential to predictive brain mapping. Hum Brain Mapp 38:5603-5615, 2017. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Dorian Pustina
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania.,Department of Radiology, Penn Image Computing and Science Lab, University of Pennsylvania, Pennsylvania
| | | | - Lyle Ungar
- Department of Computer and Information Science Department, University of Pennsylvania, Pennsylvania
| | | | - John D Medaglia
- Department of Psychology, University of Pennsylvania, Pennsylvania
| | - Brian Avants
- Department of Radiology, Penn Image Computing and Science Lab, University of Pennsylvania, Pennsylvania
| | - Myrna F Schwartz
- Moss Rehabilitation Research Institute, Elkins Park, Pennsylvania
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16
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He X, Doucet GE, Pustina D, Sperling MR, Sharan AD, Tracy JI. Presurgical thalamic "hubness" predicts surgical outcome in temporal lobe epilepsy. Neurology 2017; 88:2285-2293. [PMID: 28515267 DOI: 10.1212/wnl.0000000000004035] [Citation(s) in RCA: 109] [Impact Index Per Article: 15.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2016] [Accepted: 03/14/2017] [Indexed: 12/29/2022] Open
Abstract
OBJECTIVE To characterize the presurgical brain functional architecture presented in patients with temporal lobe epilepsy (TLE) using graph theoretical measures of resting-state fMRI data and to test its association with surgical outcome. METHODS Fifty-six unilateral patients with TLE, who subsequently underwent anterior temporal lobectomy and were classified as obtaining a seizure-free (Engel class I, n = 35) vs not seizure-free (Engel classes II-IV, n = 21) outcome at 1 year after surgery, and 28 matched healthy controls were enrolled. On the basis of their presurgical resting-state functional connectivity, network properties, including nodal hubness (importance of a node to the network; degree, betweenness, and eigenvector centralities) and integration (global efficiency), were estimated and compared across our experimental groups. Cross-validations with support vector machine (SVM) were used to examine whether selective nodal hubness exceeded standard clinical characteristics in outcome prediction. RESULTS Compared to the seizure-free patients and healthy controls, the not seizure-free patients displayed a specific increase in nodal hubness (degree and eigenvector centralities) involving both the ipsilateral and contralateral thalami, contributed by an increase in the number of connections to regions distributed mostly in the contralateral hemisphere. Simulating removal of thalamus reduced network integration more dramatically in not seizure-free patients. Lastly, SVM models built on these thalamic hubness measures produced 76% prediction accuracy, while models built with standard clinical variables yielded only 58% accuracy (both were cross-validated). CONCLUSIONS A thalamic network associated with seizure recurrence may already be established presurgically. Thalamic hubness can serve as a potential biomarker of surgical outcome, outperforming the clinical characteristics commonly used in epilepsy surgery centers.
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Affiliation(s)
- Xiaosong He
- From the Departments of Neurology (X.H., M.R.S., J.I.T.) and Neurosurgery (A.D.S.), Thomas Jefferson University, Philadelphia, PA; Department of Psychiatry (G.E.D.), Icahn School of Medicine at Mount Sinai, New York, NY; and Departments of Neurology and Radiology (D.P.), University of Pennsylvania, Philadelphia
| | - Gaelle E Doucet
- From the Departments of Neurology (X.H., M.R.S., J.I.T.) and Neurosurgery (A.D.S.), Thomas Jefferson University, Philadelphia, PA; Department of Psychiatry (G.E.D.), Icahn School of Medicine at Mount Sinai, New York, NY; and Departments of Neurology and Radiology (D.P.), University of Pennsylvania, Philadelphia
| | - Dorian Pustina
- From the Departments of Neurology (X.H., M.R.S., J.I.T.) and Neurosurgery (A.D.S.), Thomas Jefferson University, Philadelphia, PA; Department of Psychiatry (G.E.D.), Icahn School of Medicine at Mount Sinai, New York, NY; and Departments of Neurology and Radiology (D.P.), University of Pennsylvania, Philadelphia
| | - Michael R Sperling
- From the Departments of Neurology (X.H., M.R.S., J.I.T.) and Neurosurgery (A.D.S.), Thomas Jefferson University, Philadelphia, PA; Department of Psychiatry (G.E.D.), Icahn School of Medicine at Mount Sinai, New York, NY; and Departments of Neurology and Radiology (D.P.), University of Pennsylvania, Philadelphia
| | - Ashwini D Sharan
- From the Departments of Neurology (X.H., M.R.S., J.I.T.) and Neurosurgery (A.D.S.), Thomas Jefferson University, Philadelphia, PA; Department of Psychiatry (G.E.D.), Icahn School of Medicine at Mount Sinai, New York, NY; and Departments of Neurology and Radiology (D.P.), University of Pennsylvania, Philadelphia
| | - Joseph I Tracy
- From the Departments of Neurology (X.H., M.R.S., J.I.T.) and Neurosurgery (A.D.S.), Thomas Jefferson University, Philadelphia, PA; Department of Psychiatry (G.E.D.), Icahn School of Medicine at Mount Sinai, New York, NY; and Departments of Neurology and Radiology (D.P.), University of Pennsylvania, Philadelphia.
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17
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Pustina D, Coslett HB, Turkeltaub PE, Tustison N, Schwartz MF, Avants B. Automated segmentation of chronic stroke lesions using LINDA: Lesion identification with neighborhood data analysis. Hum Brain Mapp 2016; 37:1405-21. [PMID: 26756101 DOI: 10.1002/hbm.23110] [Citation(s) in RCA: 85] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2015] [Accepted: 12/21/2015] [Indexed: 12/12/2022] Open
Abstract
The gold standard for identifying stroke lesions is manual tracing, a method that is known to be observer dependent and time consuming, thus impractical for big data studies. We propose LINDA (Lesion Identification with Neighborhood Data Analysis), an automated segmentation algorithm capable of learning the relationship between existing manual segmentations and a single T1-weighted MRI. A dataset of 60 left hemispheric chronic stroke patients is used to build the method and test it with k-fold and leave-one-out procedures. With respect to manual tracings, predicted lesion maps showed a mean dice overlap of 0.696 ± 0.16, Hausdorff distance of 17.9 ± 9.8 mm, and average displacement of 2.54 ± 1.38 mm. The manual and predicted lesion volumes correlated at r = 0.961. An additional dataset of 45 patients was utilized to test LINDA with independent data, achieving high accuracy rates and confirming its cross-institutional applicability. To investigate the cost of moving from manual tracings to automated segmentation, we performed comparative lesion-to-symptom mapping (LSM) on five behavioral scores. Predicted and manual lesions produced similar neuro-cognitive maps, albeit with some discussed discrepancies. Of note, region-wise LSM was more robust to the prediction error than voxel-wise LSM. Our results show that, while several limitations exist, our current results compete with or exceed the state-of-the-art, producing consistent predictions, very low failure rates, and transferable knowledge between labs. This work also establishes a new viewpoint on evaluating automated methods not only with segmentation accuracy but also with brain-behavior relationships. LINDA is made available online with trained models from over 100 patients.
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Affiliation(s)
- Dorian Pustina
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania.,Penn Image Computing and Science Lab, Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania
| | - H Branch Coslett
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Peter E Turkeltaub
- Department of Neurology, Georgetown University, Washington, DC.,Research Division, MedStar National Rehabilitation Hospital, Washington, DC
| | - Nicholas Tustison
- Department of Radiology and Medical Imaging, University of Virginia, Virginia
| | - Myrna F Schwartz
- Language and Aphasia Lab, Moss Rehabilitation Research Institute, Elkins Park, Pennsylvania
| | - Brian Avants
- Penn Image Computing and Science Lab, Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania.,Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania
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Pustina D, Avants B, Sperling M, Gorniak R, He X, Doucet G, Barnett P, Mintzer S, Sharan A, Tracy J. Predicting the laterality of temporal lobe epilepsy from PET, MRI, and DTI: A multimodal study. Neuroimage Clin 2015; 9:20-31. [PMID: 26288753 PMCID: PMC4536304 DOI: 10.1016/j.nicl.2015.07.010] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2015] [Revised: 07/11/2015] [Accepted: 07/19/2015] [Indexed: 01/09/2023]
Abstract
Pre-surgical evaluation of patients with temporal lobe epilepsy (TLE) relies on information obtained from multiple neuroimaging modalities. The relationship between modalities and their combined power in predicting the seizure focus is currently unknown. We investigated asymmetries from three different modalities, PET (glucose metabolism), MRI (cortical thickness), and diffusion tensor imaging (DTI; white matter anisotropy) in 28 left and 30 right TLE patients (LTLE and RTLE). Stepwise logistic regression models were built from each modality separately and from all three combined, while bootstrapped methods and split-sample validation verified the robustness of predictions. Among all multimodal asymmetries, three PET asymmetries formed the best predictive model (100% success in full sample, >95% success in split-sample validation). The combinations of PET with other modalities did not perform better than PET alone. Probabilistic classifications were obtained for new clinical cases, which showed correct lateralization for 7/7 new TLE patients (100%) and for 4/5 operated patients with discordant or non-informative PET reports (80%). Metabolism showed closer relationship with white matter in LTLE and closer relationship with gray matter in RTLE. Our data suggest that metabolism is a powerful modality that can predict seizure laterality with high accuracy, and offers high value for automated predictive models. The side of epileptogenic focus can affect the relationship of metabolism with brain structure. The data and tools necessary to obtain classifications for new TLE patients are made publicly available.
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Affiliation(s)
- Dorian Pustina
- Department of Neurology, University of Pennsylvania, Philadelphia, USA
| | - Brian Avants
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Michael Sperling
- Department of Neurology, Thomas Jefferson University/Sidney Kimmel Medical College, Philadelphia, PA 19107, USA
| | - Richard Gorniak
- Department of Radiology, Thomas Jefferson University/Sidney Kimmel Medical College, Philadelphia, PA 19107, USA
| | - Xiaosong He
- Department of Neurology, Thomas Jefferson University/Sidney Kimmel Medical College, Philadelphia, PA 19107, USA
| | - Gaelle Doucet
- Department of Neurology, Thomas Jefferson University/Sidney Kimmel Medical College, Philadelphia, PA 19107, USA
| | - Paul Barnett
- Department of Neurology, Thomas Jefferson University/Sidney Kimmel Medical College, Philadelphia, PA 19107, USA
| | - Scott Mintzer
- Department of Neurology, Thomas Jefferson University/Sidney Kimmel Medical College, Philadelphia, PA 19107, USA
| | - Ashwini Sharan
- Department of Neurosurgery, Thomas Jefferson University, Philadelphia, USA
| | - Joseph Tracy
- Department of Neurology, Thomas Jefferson University/Sidney Kimmel Medical College, Philadelphia, PA 19107, USA
- Department of Radiology, Thomas Jefferson University/Sidney Kimmel Medical College, Philadelphia, PA 19107, USA
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Doucet GE, Pustina D, Skidmore C, Sharan A, Sperling MR, Tracy JI. Resting-state functional connectivity predicts the strength of hemispheric lateralization for language processing in temporal lobe epilepsy and normals. Hum Brain Mapp 2014; 36:288-303. [PMID: 25187327 DOI: 10.1002/hbm.22628] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2014] [Revised: 08/12/2014] [Accepted: 08/28/2014] [Indexed: 11/10/2022] Open
Abstract
In temporal lobe epilepsy (TLE), determining the hemispheric specialization for language before surgery is critical to preserving a patient's cognitive abilities post-surgery. To date, the major techniques utilized are limited by the capacity of patients to efficiently realize the task. We determined whether resting-state functional connectivity (rsFC) is a reliable predictor of language hemispheric dominance in right and left TLE patients, relative to controls. We chose three subregions of the inferior frontal cortex (pars orbitalis, pars triangularis, and pars opercularis) as the seed regions. All participants performed both a verb generation task and a resting-state fMRI procedure. Based on the language task, we computed a laterality index (LI) for the resulting network. This revealed that 96% of the participants were left-hemisphere dominant, although there remained a large degree of variability in the strength of left lateralization. We tested whether LI correlated with rsFC values emerging from each seed. We revealed a set of regions that was specific to each group. Unique correlations involving the epileptic mesial temporal lobe were revealed for the right and left TLE patients, but not for the controls. Importantly, for both TLE groups, the rsFC emerging from a contralateral seed was the most predictive of LI. Overall, our data depict the broad patterns of rsFC that support strong versus weak left hemisphere language laterality. This project provides the first evidence that rsFC data may potentially be used on its own to verify the strength of hemispheric dominance for language in impaired or pathologic populations.
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Affiliation(s)
- Gaëlle E Doucet
- Department of Neurology, Thomas Jefferson University, Philadelphia, Pennsylvania; Department of Neurosurgery, Thomas Jefferson University, Philadelphia, Pennsylvania
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Pustina D, Doucet G, Sperling M, Sharan A, Tracy J. Increased microstructural white matter correlations in left, but not right, temporal lobe epilepsy. Hum Brain Mapp 2014; 36:85-98. [PMID: 25137314 DOI: 10.1002/hbm.22614] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2014] [Revised: 07/04/2014] [Accepted: 08/11/2014] [Indexed: 11/06/2022] Open
Abstract
Microstructural white matter tract correlations have been shown to reflect known patterns of phylogenetic development and functional specialization in healthy subjects. The aim of this study was to establish intertract correlations in a group of controls and to examine potential deviations from normality in temporal lobe epilepsy (TLE). We investigated intertract correlations in 28 healthy controls, 21 left TLE (LTLE) and 23 right TLE (RTLE). Nine tracts were investigated, comprising the parahippocampal fasciculi, the uncinate fasciculi, the arcuate fasciculi, the frontoparietal tracts, and the fornix. An abnormal increase in tract correlations was observed in LTLE, while RTLE showed intertract correlations similar to controls. In the control group, tract correlations increased with increasing fractional anisotropy (FA), while in the TLE groups tract correlations increased with decreasing FA. Cluster analyses revealed agglomeration of bilateral pairs of homologous tracts in healthy subjects, with such pairs separated in our LTLE and RTLE groups. Discriminant analyses aimed at distinguishing LTLE from RTLE, revealing that tract correlations produce higher rates of accurate group classification than FA values. Our results confirm and extend previous work by showing that LTLE compared to RTLE patients display not only more extensive losses in microstructural orientation but also more aberrant intertract correlations. Aberrant correlations may be related to pathologic processes (i.e., seizure spread) or to adaptive processes aimed at preserving key cognitive functions. Our data suggest that tract correlations may have predictive value in distinguishing LTLE from RTLE, potentially moving diffusion imaging to a place of greater prominence in clinical practice.
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Affiliation(s)
- Dorian Pustina
- Department of Neurology, Thomas Jefferson University, Philadelphia, Pennsylvania
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Pustina D, Doucet G, Evans J, Sharan A, Sperling M, Skidmore C, Tracy J. Distinct types of white matter changes are observed after anterior temporal lobectomy in epilepsy. PLoS One 2014; 9:e104211. [PMID: 25089698 PMCID: PMC4121328 DOI: 10.1371/journal.pone.0104211] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2013] [Accepted: 07/10/2014] [Indexed: 11/18/2022] Open
Abstract
Anterior temporal lobectomy (ATL) is commonly adopted to control medically intractable temporal lobe epilepsy (TLE). Depending on the side of resection, the degree to which Wallerian degeneration and adaptive plasticity occur after ATL has important implications for understanding cognitive and clinical outcome. We obtained diffusion tensor imaging from 24 TLE patients (12 left) before and after surgery, and 12 matched controls at comparable time intervals. Voxel-based analyses were performed on fractional anisotropy (FA) before and after surgery. Areas with postoperative FA increase were further investigated to distinguish between genuine plasticity and processes related to the degeneration of crossing fibers. Before surgery, both patient groups showed bilateral reduced FA in numerous tracts, but left TLE patients showed more extensive effects, including language tracts in the contralateral hemisphere (superior longitudinal fasciculus and uncinate). After surgery, FA decreased ipsilaterally in both ATL groups, affecting the fornix, uncinate, stria terminalis, and corpus callosum. FA increased ipsilaterally along the superior corona radiata in both left and right ATL groups, exceeding normal FA values. In these clusters, the mode of anisotropy increased as well, confirming fiber degeneration in an area with crossing fibers. In left ATL patients, pre-existing low FA values in right superior longitudinal and uncinate fasciculi normalized after surgery, while MO values did not change. Preoperative verbal fluency correlated with FA values in all areas that later increased FA in left TLE patients, but postoperative verbal fluency correlated only with FA of the right superior longitudinal fasciculus. Our results demonstrate that genuine reorganization occurs in non-dominant language tracts after dominant hemisphere resection, a process that may help implement the inter-hemispheric shift of language activation found in fMRI studies. The results indicate that left TLE patients, despite showing more initial white matter damage, have the potential for greater adaptive changes postoperatively than right TLE patients.
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Affiliation(s)
- Dorian Pustina
- Thomas Jefferson University, Department of Neurology, Philadelphia, Pennsylvania, United States of America
| | - Gaelle Doucet
- Thomas Jefferson University, Department of Neurology, Philadelphia, Pennsylvania, United States of America
- Thomas Jefferson University, Department of Neurosurgery, Philadelphia, Pennsylvania, United States of America
| | - James Evans
- Thomas Jefferson University, Department of Neurosurgery, Philadelphia, Pennsylvania, United States of America
| | - Ashwini Sharan
- Thomas Jefferson University, Department of Neurosurgery, Philadelphia, Pennsylvania, United States of America
| | - Michael Sperling
- Thomas Jefferson University, Department of Neurology, Philadelphia, Pennsylvania, United States of America
| | - Christopher Skidmore
- Thomas Jefferson University, Department of Neurology, Philadelphia, Pennsylvania, United States of America
| | - Joseph Tracy
- Thomas Jefferson University, Department of Neurology, Philadelphia, Pennsylvania, United States of America
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Pustina D, Doucet G, Skidmore C, Sperling M, Tracy J. Contralateral interictal spikes are related to tapetum damage in left temporal lobe epilepsy. Epilepsia 2014; 55:1406-14. [PMID: 25041176 DOI: 10.1111/epi.12721] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/03/2014] [Indexed: 11/27/2022]
Abstract
OBJECTIVE In temporal lobe epilepsy (TLE), the epileptogenic focus is focal and unilateral in the majority of patients. A key characteristic of focal TLE is the presence of subclinical epileptiform activity in both the ictal and contralateral "healthy" hemisphere. Such interictal activity is clinically important, as it may reflect the spread of pathology, potentially leading to secondary epileptogenesis. The role played by white matter pathways in this process is unknown. METHODS We compared three interhemispheric white matter tracts (anterior commissure, fornix, and tapetum) to determine the pathway most associated with the presence of contralateral interictal spikes. Forty patients with unilateral left or right TLE were categorized based on the presence or absence of contralateral interictal spikes. Analyses of variance (ANOVAs) were run on diffusion properties from each tract. RESULTS The analyses revealed that patients with left TLE and with bilateral interictal spikes had lower fractional anisotropy (FA) and higher mean diffusivity (MD) in the tapetum. Patients with right TLE did not show this effect. No significant associations with bilateral activity were observed for the other tracts. Blood oxygen level-dependent (BOLD) functional connectivity data revealed that homotopic lateral, not mesial, temporal areas were reliably correlated in bilateral patients, independent of ictal side. SIGNIFICANCE Our results indicate that, among the tracts investigated, only the tapetum was associated with contralateral epileptiform activity, implicating this structure in seizures and possible secondary epileptogenesis. We describe two mechanisms that might explain this association (the interruption of inhibitory signals or the toxic effect of carrying epileptiform signals toward the healthy hemisphere), but also acknowledge other rival factors that may be at work. We also report that patients with TLE with bilateral spikes had increased lateral bitemporal lobe connectivity. Our current results can be seen as bringing together important functional and structural data to elucidate the basis of contralateral interictal activity in focal, unilateral epilepsy. A PowerPoint slide summarizing this article is available for download in the Supporting Information section here.
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Affiliation(s)
- Dorian Pustina
- Department of Neurology, Thomas Jefferson University, Philadelphia, Pennsylvania, U.S.A
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Doucet GE, Skidmore C, Evans J, Sharan A, Sperling MR, Pustina D, Tracy JI. Temporal lobe epilepsy and surgery selectively alter the dorsal, not the ventral, default-mode network. Front Neurol 2014; 5:23. [PMID: 24653713 PMCID: PMC3948047 DOI: 10.3389/fneur.2014.00023] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2013] [Accepted: 02/21/2014] [Indexed: 11/13/2022] Open
Abstract
The default-mode network (DMN) is a major resting-state network. It can be divided in two distinct networks: one is composed of dorsal and anterior regions [referred to as the dorsal DMN (dDMN)], while the other involves the more posterior regions [referred to as the ventral DMN (vDMN)]. To date, no studies have investigated the potentially distinct impact of temporal lobe epilepsy (TLE) on these networks. In this context, we explored the effect of TLE and anterior temporal lobectomy (ATL) on the dDMN and vDMN. We utilized two resting-state fMRI sessions from left, right TLE patients (pre-/post-surgery) and normal controls (sessions 1/2). Using independent component analysis, we identified the two networks. We then evaluated for differences in spatial extent for each network between the groups, and across the scanning sessions. The results revealed that, pre-surgery, the dDMN showed larger differences between the three groups than the vDMN, and more particularly between right and left TLE than between the TLE patients and controls. In terms of change post-surgery, in both TLE groups, the dDMN also demonstrated larger changes than the vDMN. For the vDMN, the only changes involved the resected temporal lobe for each ATL group. For the dDMN, the left ATL group showed post-surgical increases in several regions outside the ictal temporal lobe. In contrast, the right ATL group displayed a large reduction in the frontal cortex. The results highlight that the two DMNs are not impacted by TLE and ATL in an equivalent fashion. Importantly, the dDMN was the more affected, with right ATL having a more deleterious effects than left ATL. We are the first to highlight that the dDMN more strongly bears the negative impact of TLE than the vDMN, suggesting there is an interaction between the side of pathology and DM sub-network activity. Our findings have implications for understanding the impact TLE and subsequent ATL on the functions implemented by the distinct DMNs.
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Affiliation(s)
- Gaelle Eve Doucet
- Department of Neurology, Thomas Jefferson University , Philadelphia, PA , USA ; Department of Neurosurgery, Thomas Jefferson University , Philadelphia, PA , USA
| | | | - James Evans
- Department of Neurosurgery, Thomas Jefferson University , Philadelphia, PA , USA
| | - Ashwini Sharan
- Department of Neurosurgery, Thomas Jefferson University , Philadelphia, PA , USA
| | - Michael R Sperling
- Department of Neurology, Thomas Jefferson University , Philadelphia, PA , USA
| | - Dorian Pustina
- Department of Neurology, Thomas Jefferson University , Philadelphia, PA , USA
| | - Joseph I Tracy
- Department of Neurology, Thomas Jefferson University , Philadelphia, PA , USA
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