What can connectomics tell us about latelife depression? Olusola Ajilore, M.D., Ph.D. Assistant Professor ADAA 2014

Transcription

1 What can connectomics tell us about latelife depression? Olusola Ajilore, M.D., Ph.D. Assistant Professor ADAA 2014

2 Disclosures No Financial Conflict of Interests Funding NIMH

3 Overview Background Structural Studies GM networks WM/DTI networks Functional Studies Multimodal Conclusions/Future directions

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5 As yet the chemistry and mechanics of psychical processes are matters of surmise and theory; all we can say is, that mental action is a function of connections we have a right to infer that when the continuity of these connections is destroyed, interrupted or structurally impaired, modification of function must ensue. Sir John Batty Tuke, The insanity of over-exertion of the brain, 1894

6 Connectome - a comprehensive structural description of the network of elements and connections forming the human brain. Sporns, Tononi, & Kotter, PLOS Comp. Bio, 2005

7 Connectomics From nano/micro Lichtman et al, Nature Reviews Neuroscience 2008

8 To macro Connectomics

9 Nodes Edges (or Vertices)

10 Degree # of links to the network Density or Cost number of edges in a network as a proportions of all possible edges Strength the total weight of all edges

11 Functional segregation Clustering Coefficient (C) - # of connections to nearest neighbors of a node as a proportion of max. connections

12 Functional segregation Modularity Hierarchical clustering based on densely interconnected nodes with few connections between nodes in different modules

13 Functional Integration Path Length (L) - # of edges between nodes Efficiency inversely related to path length

14 Functional Influence Centrality - # of paths that pass through a node Hubs highly influential nodes

15 Small-world Networks Strangers linked by mutual acquaintance Milgram (1967) six degrees of separation

16 Basic Network Types

17 Small-world Networks Watts and Strogatz (1998) Small world networks have characteristics in between regular and random networks g = C real /C rand > 1 l = L real /L rand ~ 1 s = g/l > 1 measure of small-worldness Network design allows for segregation and distribution of information

18 Small-world Networks Examples Actors Power grids C. elegans nervous system Neuroanatomical connections Cats Macaques Humans

19 Modalities Used in Network Analysis Studies Structural MRI Functional MRI DTI EEG MEG

20 Tractography

21 Network Construction (Track count) Isthmus Anterior Medial OFC Superior Parietal Isthmus 0 Anterior 0 Medial OFC 0 Superior Parietal 0

22 Network Construction (Track count) Isthmus Anterior Medial OFC Superior Parietal Isthmus 0 Anterior 0 Medial OFC 0 Superior Parietal 0

23 Network Construction (Track count) Isthmus Anterior Medial OFC Superior Parietal Isthmus 0 Anterior 0 Medial OFC 0 Superior Parietal 0

24 Network Construction (Track count) Isthmus Anterior Medial OFC Superior Parietal Isthmus Anterior Medial OFC 0 Superior Parietal 0

25 Network Construction (Track count) Isthmus Anterior Medial OFC Superior Parietal Isthmus Anterior Medial OFC 0 Superior Parietal 0

26 Network Construction (Track count) Isthmus Anterior Medial OFC Superior Parietal Isthmus Anterior Medial OFC 0 Superior Parietal 0

27 Network Construction (Track count) Isthmus Anterior Medial OFC Superior Parietal Isthmus Anterior Medial OFC 0 14 Superior Parietal 14 0

28 Network Construction (Track count) Isthmus Anterior Medial OFC Superior Parietal Isthmus Anterior Medial OFC Superior Parietal 14 0

29 Network Construction

30 Background Overview Structural Studies GM networks WM/DTI networks Functional Studies Multimodal Conclusions/Future directions

31 Demographics Healthy Controls Late-Life Depressed N Age (7.16) (8.52) Gender (M/F) 23/50 18/35 Race (White/Non- 58/15 47/6 White) Education (years) (2.20) (2.94) GDS* 2.32 (2.66) (5.26) BDI* 2.75 (4.26) (9.11) Ajilore et al, AJGP 2014b

32 Connectivity Matrices Healthy Control Late-Life Depressed Ajilore et al, AJGP 2014b

33 Normalized Global Efficiency Background Network Analysis Conclusions Network Efficiency HC LLD p< Network Density Ajilore et al, AJGP 2014b

34 Network Influence Left Paracentral Gyrus Left Postcentral Gyrus Left Fusiform Left Hippocampus Right Precentral Right Inferior Parietal Right Precuneus Right Bank of the Superior Temporal Sulcus Right Entorhinal Right Insula Right Lingual Right Hippocampus Right Amygdala Ajilore et al, AJGP 2014b

35 Relative Size of Largest Component Background Network Analysis Conclusions Network Resilience 1.2 Random Failure Analysis HC LLD p < Fraction of Removed Nodes Ajilore et al, AJGP 2014b

36 Relative Size of Largest Component Background Network Analysis Conclusions Network Resilience 1.2 Targeted Failure Analysis HC LLD p < Fraction of Removed Nodes Ajilore et al, AJGP 2014b

37 Relative Size of Largest Component Background Network Analysis Conclusions Network Resilience 1.2 Targeted Failure Analysis c/f WMH HC LLD p < Fraction of Removed Nodes Ajilore et al, AJGP 2014b

38 Background Overview Structural Studies GM networks WM/DTI networks Functional Studies Multimodal Conclusions/Future directions

39 Network Efficiency in WM networks No Difference between HC (n = 43) and MDD (n = 40) Ajilore et al, AJGP 2014a

40 Network Efficiency in WM networks Significantly correlated with age (r = -.43, p <.0001) Ajilore et al, AJGP 2014a

41 Network Efficiency in WM networks Significantly correlated with depression severity (r = -.34, p =.03) Ajilore et al, AJGP 2014a

42 Background Overview Structural Studies GM networks WM/DTI networks Functional Studies Multimodal Conclusions/Future directions

43 LLD rs-fmri data No global differences Significant differences in right caudate connectivity Bohr et al, Frontiers in Psychiatry,2013

44 LLD rs-fmri data Bilateral superior frontal r. rostral acc Right mofc r. Accumbens area Tadayon-Nejad et al, under review

45 Background Overview Structural Studies GM networks WM/DTI networks Functional Studies Multimodal Conclusions/Future directions

46 rs-fmri Correlation Background Network Analysis Conclusions Functional by Structural Hierarchical (FSH) Mapping level of rsfmri correlation between i and j e kf i,j U D Modified Graph Distance Ajilore et al, Frontiers in Neuroinformatics 2013

47 Direct Structural Connections Observed FC (z-score) Observerd FC (z-score) Background Network Analysis Conclusions Functional by Structural Hierarchical (FSH) Mapping Fitting without Utilization Matrix Fitting with Utilization Matrix r =.287, p <.0001 r =.472, p <.0001 Predicted FC (z-score) Predicted FC (z-score) Ajilore et al, Frontiers in Neuroinformatics 2013

48 CLICK TO EDIT MASTER SYLE Functional by Structural Hierarchical (FSH) Mapping Subject Sample 7 HC (age 65.6±8.1, 4 males) / 7 LLD (age 60.7±2.9, 4 males) Mean HAM-D 20±3.7 (in LLD subjects) Ajilore et al, Frontiers in Neuroinformatics 2013

49 Functional by Structural Hierarchical (FSH) Mapping Left Posterior Ajilore et al, Frontiers in Neuroinformatics 2013

50 Functional by Structural Hierarchical (FSH) Mapping Right Posterior Ajilore et al, Frontiers in Neuroinformatics 2013

51 Functional by Structural Hierarchical (FSH) Mapping Right Posterior Ajilore et al, Frontiers in Neuroinformatics 2013

52 Functional by Structural Hierarchical (FSH) Mapping Right Precuneus Ajilore et al, Frontiers in Neuroinformatics 2013

53 rs-sc Structural Functional HC LLD Ajilore et al, Frontiers in Neuroinformatics 2013

54 Summary of findings Impaired network efficiency (in GM networks) and resiliency partially mediated by WMH burden in LLD Altered network influence and community structure implicating key regions in the default mode network

55 Background Overview Structural Studies GM networks WM/DTI networks Functional Studies Multimodal Conclusions/Future directions

56 Critiques Background Network Analysis Conclusions Too mathematical Too abstract from real data What s the point???

57 Clinical Implications Characterization of the impact of specific regions on whole-brain connectivity.

58 Clinical Implications Characterization of the impact of specific regions on whole-brain connectivity. Identification of abnormal circuits which could lead to patient-specific targets for future interventions, such as brain stimulation techniques or psychotherapies designed to engage these circuits

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60 Future directions Clinical/cognitive correlates Treatment response Validate in larger datasets Stochastic, dynamic graph models User-friendly connectome tools

61 PLACE Background Network Analysis Conclusions

62 CoNECt Toolbox

63 Acknowledgments Alex Leow, MD, PhD Johnson GadElkarim, PhD Shaolin Yang, PhD Aifeng Zhang, PhD Melissa Lamar, PhD Rebecca Charlton, PhD Emma Rhodes, MA Mai Lynn Grajewski, LCSW Laura Korthauer, MA Piotr Daranowski, MA Jennifer Medina, PhD Anand Kumar, MD Subjects who volunteered countless hours of time and effort! - brain.uic.edu This research was funded by the National Institute of Health (R01-MH-63674, MH-55115, MH-61567, MH-02043, MH081175) awarded to Anand Kumar, MD and Olu Ajilore, MD, PhD