Effects of Omega-3 Fatty Acids on Brain Connectivity in Long-Evans Rats
Adebayo B Braimah1,2, Diana M Lindquist1, Ruth Asch3, Jennifer Schurdak3, and Robert McNamara3

1Imaging Research Center, Department of Radiology, Cincinnati Children's, Cinicinnati, OH, United States, 2Pediatric Neuroimaging Research Consortium, Cincinnati Children's, Cinicinnati, OH, United States, 3UC Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, United States


This study examines the impact of dietary fatty acid intake on functional connectivity of the maturing brain. This study was performed with 88 rats. In vivo as well as ex vivo neurological data was collected by means of MR imaging and postmortem gas chromatography. The graphs were compared by using network-based statistics and showed non-significant trends between the controls and the fatty-acids deficient group. Network metrics were also computed and showed non-significant trends between the controls and the fatty acids deficient group.


Major psychiatric disorders including bipolar disorder frequently initially emerge in childhood and adolescence1-8, a developmental period associated with the rapid accumulation of the omega-3 polyunsaturated fatty acid docosahexaenoic acid (DHA, 22:6n-3) in the brain2-4,9-11, as well as maturational changes in functional connectivity9. Although psychiatric disorders are associated with deficits in DHA and widespread abnormalities in functional connectivity9,12, the causal relationship has not been systematically investigated. The present study determined the effect of dietary-induced alterations in brain DHA accrual during perinatal development on functional connectivity in young adult rat brain.


Dams were provided either control diet that contained omega-3 fatty acids or diet deficient in omega-3 fatty acids for 30 days prior to mating and during gestation and lactation. At weaning, pups from control dams remained on the control diet (group CC, N = 33), and pups from deficient dams were weaned onto either the deficient diet (group DD, N = 28) or a diet fortified with fish oil which contains preformed DHA (group DF, N = 27). On P90, rsphMRI scans were performed under isoflurane anesthesia in a 7T Bruker Biospec system. Postmortem brain fatty acid composition was determined by gas chromatography.MRI analysis utilized many of FSL’s13 and AFNI’s14 software in addition to the Brain Connectivity Toolbox15, BrainNet Viewer 16, and the Network-Based Statistics17 (NBS) Toolbox. The images then were scaled by a factor of 10. The Waxholm Space18 (WHS) rat template and its ROIs were similarly scaled, changing the image dimensions from 512 x 1024 x 512 (0.039 mm isotropic voxel resolution) to 110 x 256 x 128 (2 mm isotropic voxel resolution). Standard preprocessing steps were undertaken, including skull stripping, slice timing correction, and spatial normalization of the rat brain to the Waxholm Space (WHS) template. The functional scans were then affine registered to the template. Additional preprocessing steps of the functional data included despiking, outlier identification and motion correction, CompCorr (component-based correction, which included regressing out motion, outlier volumes, and the top 5 principle components of the thresholded and eroded WM and CSF masks), and bandpass filtering (0.009 < f < 0.08 Hz). Quality control was performed via visual inspection, checking segmentations, and structural and functional registrations to template. Only the first 125 time points were of interest, as the remaining time points were related to a pharmaco-MRI study.Graph theory analyses were performed and began by extracting the rats’ timeseries data in relation to the WHS ROIs (115 ROIs), followed by constructing binary, undirected networks. The graph metrics that were analyzed were: global efficiency, normalized closeness centrality, normalized path length (λ), normalized clustering coefficient (γ), and small-world index (σ). Statistics of the metrics were computed with a 3-group design F-test, and a low threshold of 1.5 to account for the decreased number of time points.


Compared with CC rats, DD rats exhibited robust deficits in cortical DHA levels, and DF rat exhibited DHA levels that were similar to CC rats. Post-QC, 72% of the scans (63, CC = 22, DD = 16, DF = 25) were considered for analysis. The findings showed a non-significant trend for a difference between CC and DF rats (p = 0.0647). The CC and DD (p=0.2269), and the DD and DF (p=0.1246) showed no clear trends (Figures 1 and 2). The graph metrics measured showed non-significant trends in the case of the CC and DF rats.


The present preliminary findings provide limited support for a role of dietary DHA intake and brain accrual on the maturation of functional connectivity in rat brain. Nevertheless, additional investigation using a study-specific template is ongoing and may lead to different results. The Non-significant trends in the case of the CC and DF rats network measures could imply that there were little-to-no detectable differences in the functional connectivity networks; likely due to the decreased number of time points analyzed (Figures 3 and 4). Significant differences between any of the three groups of rats would have implied abnormal functional connectivity between brain regions, which would have indicated that early dietary intake of n-3 fatty acids has a role in developing normal brain connectivity. Since no such differences were observed, it is likely that the association of n-3 deficiency to psychiatric illness is not due to alterations in brain connectivity, but rather alterations in signaling pathways or membrane structure.


Understanding the relationship between dietary omega-3 fatty acid intake during development and the maturation of brain functional connectivity may provide important opportunities for early interventions in youth that are at high-risk for developing psychiatric disorders including bipolar disorder.


We would like to acknowledge Scott Dunn and Beth Fugate for scanning the rats.


1. McNamara RK, Asch RH, Schurdak JD, Lindquist DM. Glutamate homeostasis in the adult rat prefrontal cortex is altered by cortical docosahexaenoic acid accrual during adolescence: An in vivo 1H MRS study. Psychiatry research Neuroimaging. 2017;270:39-45.

2. McNamara RK, Jandacek R, Rider T, et al. Abnormalities in the fatty acid composition of the postmortem orbitofrontal cortex of schizophrenic patients: gender differences and partial normalization with antipsychotic medications. Schizophrenia research. 2007;91(1-3):37-50.

3. McNamara RK, Jandacek R, Rider T, et al. Deficits in docosahexaenoic acid and associated elevations in the metabolism of arachidonic acid and saturated fatty acids in the postmortem orbitofrontal cortex of patients with bipolar disorder. Psychiatry research. 2008;160(3):285-299.

4. McNamara RK, Welge JA. Meta-analysis of erythrocyte polyunsaturated fatty acid biostatus in bipolar disorder. Bipolar disorders. 2016;18(3):300-306.

5. McNamara RK, Carlson SE. Role of omega-3 fatty acids in brain development and function: potential implications for the pathogenesis and prevention of psychopathology. Prostaglandins, leukotrienes, and essential fatty acids. 2006;75(4-5):329-349.

6. McNamara RK, Vannest JJ, Valentine CJ. Role of perinatal long-chain omega-3 fatty acids in cortical circuit maturation: Mechanisms and implications for psychopathology. World journal of psychiatry. 2015;5(1):15-34.

7. McNamara RK, Asch RH, Lindquist DM, Krikorian R. Role of polyunsaturated fatty acids in human brain structure and function across the lifespan: An update on neuroimaging findings. Prostaglandins, leukotrienes, and essential fatty acids. 2018;136:23-34.

8. McNamara RK, Hahn CG, Jandacek R, et al. Selective deficits in the omega-3 fatty acid docosahexaenoic acid in the postmortem orbitofrontal cortex of patients with major depressive disorder. Biological psychiatry. 2007;62(1):17-24.

9. Messamore E, McNamara RK. Detection and treatment of omega-3 fatty acid deficiency in psychiatric practice: Rationale and implementation. Lipids in health and disease. 2016;15:25-25.

10. McNamara RK, Jandacek R, Rider T, Tso P, Dwivedi Y, Pandey GN. Selective deficits in erythrocyte docosahexaenoic acid composition in adult patients with bipolar disorder and major depressive disorder. Journal of affective disorders. 2010;126(1-2):303-311.

11. Lindquist DM, Asch RH, Schurdak JD, McNamara RK. Effects of dietary-induced alterations in rat brain docosahexaenoic acid accrual on phospholipid metabolism and mitochondrial bioenergetics: An in vivo 31P MRS study. Journal of psychiatric research. 2017;95:143-146.

12. McNamara RK, Schurdak JD, Asch RH, Lindquist DM. Omega-3 fatty acid deficiency impairs frontostriatal recruitment following repeated amphetamine treatment in rats: A 7 Tesla in vivo phMRI study. Nutritional neuroscience. 2017:1-9.

13. Jenkinson M, Beckmann CF, Behrens TE, Woolrich MW, Smith SM. FSL. Neuroimage. 2012;62(2):782-790.

14. Cox RW. AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Computers and biomedical research, an international journal. 1996;29(3):162-173.

15. Rubinov M, Sporns O. Complex network measures of brain connectivity: uses and interpretations. Neuroimage. 2010;52(3):1059-1069.

16. Xia M, Wang J, He Y. BrainNet Viewer: a network visualization tool for human brain connectomics. PLoS One. 2013;8(7):e68910.

17. Zalesky A, Fornito A, Bullmore ET. Network-based statistic: identifying differences in brain networks. Neuroimage. 2010;53(4):1197-1207.

18. Papp EA, Leergaard TB, Calabrese E, Johnson GA, Bjaalie JG. Waxholm Space atlas of the Sprague Dawley rat brain. Neuroimage. 2014;97:374-386.

19. Bassett DS, Bullmore ET. Small-World Brain Networks Revisited. The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry. 2017;23(5):499-516.


Figure 1: Shows the group mean connectivity matrices for the CC (Figure 1A), DD (Figure 1B), and DF (Figure 1C) rats. The CC rats (Figure 1A) appear to show two communities in their connectivity networks, indicating increased within-group connections and decreased between-group connections15. The DD and DF rats (Figures 1B and 1C respectively) depict three diffuse communities, indicating decreased within-group connections and increased between-group connections15.

Figure 2: Shows the graph edges and nodes for the CC (Figure 2A), DD (Figure 2B), and DF (Figure 2C) group mean connectivity. The graphs are shown in neurological orientation (i.e. left and right are not flipped), in an axial view. Moreover, no significant differences were found in the overall graph networks between the groups.

Figure 3: The graph network metrics for global efficiency (Figure 3A), closeness centrality (Figure 3B), and normalized characteristic path length (Figure 3C). No significant differences were observed for these graph network measures between the three groups of rats. Additionally, the increased variability shown by the DD rat group (Figures 3A – C) could further contribute to the decreased significance of group differences between the DD rat groups against the others.

Figure 4: The graph network metrics for the normalized clustering coefficient (γ, Figure 4A) and the small-world index (σ, Figure 4B) for the three rat groups. Although no significant differences were found, the normalized clustering coefficients for the all three rat groups depicts a fairly large range, indicating high a degree of clustered connectivity15. Similarly, the small-world index for the entire cohort appears to show decreased variability and σ > 1, indicating a fair degree of resemblance to a small-world network topology15,19.

Proc. Intl. Soc. Mag. Reson. Med. 27 (2019)