Aribisala, Benjamin

Bastin, Mark

Cox, Simon R

Deary, Ian

Ferguson, Karen

Maclullich, Alasdair

MacPherson, Sarah E

Royle, Natalie

Starr, John

Valdes Hernandez, Maria

Wardlaw, Joanna

Edinburgh Research Explorer

     Edinburgh Research Explorer                                      Frontal Lobe IntraconnectivityCitation for published version:Cox, SR, Ferguson, K, Aribisala, B, MacPherson, SE, Valdes Hernandez, M, Royle, N, Maclullich, A, Starr,J, Deary, I, Wardlaw, J & Bastin, M 2014, 'Frontal Lobe Intraconnectivity: Short Range Tract Characteristicsin Old Age' 20th Annual Meeting of the Organization for Human Brain Mapping, Hamburg, Germany,8/06/14, pp. 1.Link:Link to publication record in Edinburgh Research ExplorerDocument Version:Peer reviewed versionPublisher Rights Statement:© Cox, S. R., Ferguson, K. J., Aribisala, B., MacPherson, S. E., Hernandez Valdez, M., Royle, N. A., ... Bastin,M. (2014). Frontal Lobe Intraconnectivity: Short Range Tract Characteristics in Old Age. 1. Poster sessionpresented at 20th Annual Meeting of the Organization for Human Brain Mapping, Hamburg, Germany.General rightsCopyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s)and / or other copyright owners and it is a condition of accessing these publications that users recognise andabide by the legal requirements associated with these rights.Take down policyThe University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorercontent complies with UK legislation. If you believe that the public display of this file breaches copyright pleasecontact openaccess@ed.ac.uk providing details, and we will remove access to the work immediately andinvestigate your claim.Download date: 05. Apr. 2019Methods  Subjects & MR Imaging • Eighty eight males from Lothian Birth Cohort 19365, mean age 73.7 ± 1 yr. • Community-dwelling, MMSE≥24, HADS<11, not on antidepressants.  • T1W scan (resolution 1x1x1.3 mm), 1.5 T GE scanner • DTI scan (resolution 2x2x2mm), 1.5T GE scanner  Structural Images • Seven gyral frontal regions were manually segmented on T1W with Analyze 8.1 using a protocol published elsewhere6 with excellent reproducibility (intra-rater ICCs > .96).  • Brain extraction (multi-spectral in Analyze).  T2*-weighted and FLAIR volumes were fused using an image fusion tool  Brain extracted using object extractor tool  Masks from this processes then applied to T1W  Diffusion Tensor Images • Motion & eddy current distortion corrected by registering all diffusion-weighted volumes to the 1st undistorted b0 image7   • DT-MRI reconstruction used interpolated streamline and fractional anisotropy (FA) computation in DTI Toolkit.  • Segmented frontal lobe regions then transformed to DT-MRI space (via T1W) using FLIRT7   • Site-to-site connection performed in TrackVis8 (www.trackvis.org). Tracts connecting each pair of manually-segmented frontal ROIs were isolated.  • Primary measures were:   Connection Probability ( # tracks connecting each pair of regions / the total # frontal lobe tracks).  Mean FA values of the connecting tracts.  Coefficient of variation (CoV) was used to index tract variation across individuals. Frontal Lobe Intraconnectivity: Short-range tract  characteristics in old age  • The frontal lobes   facilitate our most complex thinking   contain multiple cytoarchitecturally and functionally discrete regions1   regions interact via complex, short-range white matter (WM) connections  • The frontal lobes are particularly prone to age-related structural decline2  • This may partially explain age-related cognitive ability decline in the elderly3 • Most studies of the frontal lobes focus on various frontal cortical areas. • Yet, the WM connecting these regions remains relatively under-researched4 • It is unclear how individual differences in the number of connections and WM integrity in the frontal lobe vary in older age. • The results show that the measures of connections involving cingulate and lateral frontal regions are highly variable in older age. • This is a promising approach from which to examine the relationship between cognitive ability and the number, density and integrity of short range frontal lobe connections in old age.  • Longitudinal data or comparison with a younger group would help to determine if this variability is a feature of ageing, rather than pre-existing individual differences.  • More advanced tractography algorithms such as those based on probabilistic methods with 2 fibre populations per voxel will be investigated. [1] Zald, D.H. (2007). Orbital versus dorsolateral prefrontal cortex: anatomical insights into content versus differentiation models of the prefrontal cortex. Ann. of the New York Academy of Sc., vol. 1121, pp. 395-406. [2] Fjell, A.M. et al. (2009). High consistency of regional cortical thinning in aging across multiple samples. Cerebral Cortex, vol. 19, pp.2001-2012. [3] MacPherson et al. (2002). Age, executive function and social decision making: a dorsolateral prefrontal theory of cognitive aging. Psychology and Aging, vol.17, no.4, pp.598-609. [4] Catani M. et al. (2012), Short frontal lobe connections of the human brain, Cortex, vol. 48, no. 2, pp. 273-291. [5] Deary, I. J. et al. (2007). The Lothian Birth Cohort 1936: a study to examine influences on cognitive ageing from age 11 to age 70 and beyond. BMC Geriatrics, vol. 7, pp. 28. [6] Cox S.R. et al. (2014), A systematic review of brain frontal lobe parcellation techniques in magnetic resonance imaging, Brain Structure and Function vol. 219, no. 1, pp. 1-22. [7] Jenkinson, M., & Smith, S. (2001). A global optimisation method for robust affine registration of brain images. Medical Image Analysis, vol. 5, no. 2, pp. 143-156. [8] www.trackvis.org [9] Beckmann M. et al. (2009), Connectivity-Based Parcellation of Human Cingulate Cortex and Its Relation to Functional Specialization, The Journal of Neuroscience, vol. 29, no. 4, pp. 1175–1190. S.R. Cox1,2,3*, B.S. Aribisala1,2,4,5*, K.J. Ferguson2,6, S.E. MacPherson2,3, S. Muñoz Maniega1,2,5, M.C. Valdéz Hernández1,2,5, N.A.Royle1,2,5, A.M.J. MacLullich2,7,8,  J.M. Starr2,8,9, I.J. Deary2,3, J.M. Wardlaw1,2,5, M.E. Bastin1,2,5 * First Joint Authors 1Brain Research Imaging Centre, University of Edinburgh, UK, 2Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, UK, 3Department of Psychology,  University  of  Edinburgh,  UK,  4Department  of  Computer  Science, Lagos State University, Lagos, Nigeria,  5Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE), 6School of Clinical Sciences, University of Edinburgh, UK,  7Endocrinology Unit, University of Edinburgh, UK, 8Geriatric Medicine Unit, University of Edinburgh, UK,  9Alzheimer Scotland Dementia Research Centre, University of Edinburgh, UK.  Measure connectivity among frontal regions in older adults.  Characterise variation in the number, density and integrity of these tracts. Aims Introduction Results Fig. 2: Radar plot of the mean + SD (dark and light blue) connection probability (x 102) dACC:  DORSAL ANTERIOR CINGULATE,  VACC:  VENTRAL ANTERIOR CINGULATE SFG:  SUPERIOR FRONTAL GYRUS,  MFG:  MIDDLE FRONTAL GYRUS,  IFG:  INFERIOR FRONTAL GYRUS,  OFG:  ORBITOFRONTAL GYRUS,  FP:  FRONTAL POLE Fig. 3: Radar plot of mean + SD (red and orange) FA values of the tracts connecting frontal regions Correspondence:  simon.cox@ed.ac.uk                    |   7 George Square, Edinburgh, EH8 9JZ, United Kingdom |   www.ccace.ed.ac.uk  www.bric.ed.ac.uk benjamin.aribisala@lasu.edu.ng    |    Computer Science Department, Lagos State University, Lagos, Nigeria    |   www.lasu.edu.ng        References Conclusions Fig. 4: Coefficient of variation (CoV) bar chart showing variability in the connection probability and FA values of the connecting tracts, and scatter plot with regression line (inset; top right) of the association between connection probability CoV and FA values CoV. CoV of FA values range = 0% - 30% CoV of connection probability range = 0% - 250% • Tracts (Fig. 1) and connectivity profiles (Fig. 2) concurred with previous anatomical reports of healthy younger participants4,9.  • Individual variation in connection probability and tract FA (Fig. 3) was high. Particularly for lateral and cingulate regions. Variation in connectivity and FA were tightly related across frontal lobe tracts (β = .89, p<.000001) Fig. 1. Examples of intralobar frontal tracts.  Left Image Red: dACC and SFG Cyan: SFG and FP Orange: OFC and FP Yellow: vACC and OFC  Right Image Yellow: SFG to IFG Green: SFG to MFG Red: MFG to IFG 

Frontal Lobe Intraconnectivity:Short Range Tract Characteristics in Old Age

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Frontal Lobe Intraconnectivity:Short Range Tract Characteristics in Old Age

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