There are many active research projects accessing and applying shared ADNI data. Use the search above to find specific research focuses on the active ADNI investigations. This information is requested annually as a requirement for data access.
| Principal Investigator | |
| Principal Investigator's Name: | Vassilis Pelekanos |
| Institution: | University of Nottingham |
| Department: | School of Medicine |
| Country: | |
| Proposed Analysis: | Neuroimaging data can provide unique ways to probe the brain tissue microstructure and brain network connections. With the present application, we request access to the valuable ADNI neuroimaging resource so that we may compare the brain integrity and connectivity changes in people with mild cognitive impairment (MCI) and Alzheimer’s disease (AD) with the changes that emerge after selective fornix damage in non-human primates (NHP). In our research with NHPs, we have conducted neuroimaging in the macaque monkey animal model combined with behavioral and cognitive assessments on learning and memory tasks before and after a fornix transection in each animal. A separate age and cohort matched group of NHPs remained neurologically intact. We have collected T1-weighted and diffusion-weighted MRI, as well as resting-state functional MRI data from all animals to investigate structural and functional changes in the primate brain caused by fornix transection. In our latest experiments, we used 2 groups of NHPs: the first group (experimental) participated in behavioral training to learn a variant of the object-in-place scene discrimination task (OIP; [1]) that assays episodic-like memory in monkeys [2]. Subsequently, the experimental group underwent neurosurgery to receive a selective bilateral fornix transection. The second group (control) were trained on a passive visual fixation task [3], and operated under general anaesthesia to receive a head-post implanted onto the skull -but remained neurologically intact. We collected MRI data from all monkeys at various time points during the course of the experiments, both pre-operatively (before training, and at key intervals after training started) and post-operatively (at key time points following post-operative recovery). In a recent article from our group [4], we showed that, compared to the control group, training on the OIP visuo-spatial memory task induced alterations in the uncinate fasciculus (UF), fornix, and the ventral prefrontal white matter tracts, as well as functional connectivity changes within and between key fronto-temporal grey matter regions. Furthermore, we found that disrupting neural communication via the fornix caused marked changes to the training-related functional and structural profile, as well as impaired learning of new information. More recently [5], we focused on the fornix-transection-related alterations in structural connectivity quantified by probabilistic tractography. We employed the number of tracking streamlines to probe connectivity changes in the experimental group compared to the control across our regions of interest (ROIs). In the neurologically intact monkeys, we observed high connectivity across certain ROIs, including the CA3 hippocampal subfield with the retrosplenial cortex (RSC), the anterior thalamus with the RSC, as well as the RSC with the anterior cingulate cortex (ACC). However, compared to the control group, the fornix-transected monkeys showed significant connectivity changes including increases between the anterior thalamus and the ACC, and between the CA3 and the ACC, as well as decreases between the CA3 and the RSC. Building up on our previous research and expertise in neuroimaging, as well as on our experience in conducting analyses on large scale human neuroimaging datasets [UK Biobank and the Human Connectome Project (Pelekanos et al., in preparation -two papers)], in the present study we will aim to understand the associations between AD neuropathology and brain structural integrity and connectivity as depicted by imaging signatures in the ADNI, and the integrity and connectivity measurements in our NHP neuroimaging dataset. To this end, we will investigate the T1-weighted and diffusion-weighted images in both the humans and the NHPs to evaluate whether a common profile of grey matter integrity and connectivity alterations take place in AD pathology and after fornix damage. Our hypothesis is that fornix transection in the macaque and the brain pathologies in AD are likely to cause many similar changes in brain structures and networks that support normal memory function (such as the medial temporal lobes, the frontal, cingulate, and retrosplenial cortices, and the medial diencephalon). Evidence to support this hypothesis stems from the fact that damage to the fornix in both NHPs and humans has been consistently shown to cause deficits in learning and memory, and can produce anterograde amnesia [1, 6-12]. Furthermore, human MRI studies have indicated that the fornix is associated with recollection memory and recall in adults [13-15], and with episodic memory in childhood [16]. Last but not least, the fornix’s functional and anatomical features have been suggested as potential prognostic biomarkers for AD: the microstructural integrity of the fornix has been indicated as a predictive marker of memory decline [17-19], while recent clinical trials revealed that deep brain stimulation of the fornix improved cognitive functions in small groups of patients with Alzheimer’s disease [20; for a recent review see ref. 21]. We will assess grey matter integrity in both species using multi-modal MRI analysis to evaluate (i) cortical thickness, derived from the T1-weighted images, corrected for B1- inhomogeneity, (ii) intracortical myelin content, based on the same T1-weighted images [22] as well as (iii) grey-matter neurite orientation dispersion (OD) and neurite density (ND), derived from the diffusion-weighted dataset [23]. The different measures would be expected to reflect different, but related, aspects of grey matter integrity and structure [24] and will thus be analysed using multivariate tools. Grey-matter degeneration is likely related with degeneration of associated white matter tracts. To test this, we will also measure white-matter microstructural changes in tracts like the uncinate fasciculus using crossing-fibre probabilistic tractography [25] based on the diffusion-weighted images. Using the tractography results, we will extract microstructural properties, including DTI measures and higher-order metrics, such as neurite density and extracellular volume fraction from the NODDI model [23], as well as diffusional kurtosis imaging [26]. Finally, based on the diffusion-weighted images, we will also employ the tractography streamlines seeded from a range of grey matter ROIs to quantify structural connectivity changes ROI-by-ROI. We will use the same homologous ROIs in both species, selected on the basis of the ROIs’ role in higher cognition and memory. Specifically, we will test ROIs in the anterior and medial temporal lobes, the retrosplenial, cingulate and frontal cortex, and the medial diencephalon, that have been consistently shown to support memory functions in both humans and NHPs [27-33]. Given the fornix’s prominence in the limbic circuit and importance for memory, as well as the macaque NHPs’ similarity to humans in terms of physiology, neuroanatomy, and cognition, and the importance of involving the NHP model in biomedical research [e.g., 34-36], we are confident that comparing brain changes in our NHP model with those in human AD and/or MCI patients will provide further understanding about the neural degeneration associated with memory disorders. Comparative neuroimaging studies in humans and macaques have shown that diffusion MRI, in particular, can capture neuroanatomical similarities and differences between the two species [37-38], while more recent studies have developed methodology and frameworks that facilitate the understanding of similarities and differences between the brains of primates [39-40]. As a final note, we would be grateful if, apart from the raw and pre-processed MRI data (including structural, functional and diffusion-weighted modalities), we could also get access to the PET imaging dataset, as we are also planning to investigate the possible relationship between tau and Amyloid-β neuropathologies with cortical degeneration. Thank you very much for considering our application. Vassilis Pelekanos and Anna S. Mitchell References [1] Gaffan D (1994). Scene-specific memory for objects: A model of episodic memory impairment in monkeys with fornix transection. J Cogn Neurosci, 6:305–320. [2] Murray EA, Wise SP (2010). What, if anything, can monkeys tell us about human amnesia when they can’t say anything at all? Neuropsychologia, 48: 2385-2405. [3] Pelekanos V, Mok R, Joly O, Ainsworth M, Kyriazis D, Kelly M, Bell A, Kriegeskorte N (2020). Rapid event related, BOLD fMRI, non-human primates (NHP): choose two out of three. Sci Rep, 4;10(1):7485. [4] Pelekanos V, Premereur E, Mitchell D, Chakraborty S, Mason S, Lee A, Mitchell AS (2020). Cortico-cortical and thalamocortical changes in functional connectivity and white matter structural integrity after reward-guided learning of visuospatial discriminations in rhesus monkeys. J Neurosci, 40(41):7887-7901. [5] Pelekanos V, Premereur E, Mitchell AS. (In Press). Structural connectivity changes after fornix transection in macaques using probabilistic diffusion tractography. Advances in Experimental Medicine and Biology. [6] Gaffan D, Gaffan EA (1991). Amnesia in man following transection of the fornix. Brain, 114:2611-2618. [7] D’Esposito M, Verfaellie M, Alexander MP, Katz DI. (1995). Amnesia following traumatic bilateral fornix transection. Neurology, 45: 1546–1550. [8] Aggleton JP, McMackin D, Carpenter K, Hornak J, Kapur N, Halpin S et al. (2000). Differential cognitive effects of colloid cysts in the third ventricle that spare or compromise the fornix. Brain, 123 (Pt 4):800–815. [9] Charles DP, Gaffan D, Buckley MJ (2004). Impaired recency judgments and intact novelty judgments after fornix transection in monkeys. J Neurosci, 24:2037-2044. [10] Poreh A, Winocur G, Moscovitch M, Backon M, Goshen E, Ram Z, Feldman Z (2006). Anterograde and retrograde amnesia in a person with bilateral fornix lesions following removal of a colloid cyst. Neuropsychologia, 44:2241–2248. [11] Kwok SC, Mitchell AS, Buckley MJ (2015). Adaptability to changes in temporal structure is fornix-dependent. Learn Mem, 22:354-359. [12] Wilson CR, Charles DP, Buckley MJ, Gaffan D (2007). Fornix transection impairs learning of randomly changing object discriminations. J Neurosci, 27:12868-12873. [13] Tsivilis D, Vann SD, Denby C, Roberts N, Mayes AR, Montaldi D, Aggleton JP (2008). A disproportionate role for the fornix and mammillary bodies in recall versus recognition memory. Nat Neurosci, 11(7), 834-842. [14] Rudebeck SR, Scholz J, Millington R, Rohenkohl G, Johansen-Berg H, Lee AC (2009). Fornix microstructure correlates with recollection but not familiarity memory. J Neurosci, 29(47):14987-92. [15] Metzler-Baddeley C, Jones DK, Belaroussi B, Aggleton JP, O'Sullivan MJ (2011). Frontotemporal connections in episodic memory and aging: a diffusion MRI tractography study. J Neurosci, 31(37):13236-13245. [16] Hoffman LJ, Ngo CT, Canada KL, Pasternak O, Zhang F, Riggins T, Olson IR (2022). The fornix supports episodic memory during childhood. Cereb Cortex, bhac022. [17] Mielke MM, Okonkwo OC, Oishi K, Mori S, Tighe S, Miller MI et al. (2012). Fornix integrity and hippocampal volume predict memory decline and progression to Alzheimer's disease. Alzheimers Dement, 8(2):105-113. [18] Oishi K, Mielke MM, Albert M, Lyketsos CG, Mori S (2012). The fornix sign: a potential sign for Alzheimer's disease based on diffusion tensor imaging. J Neuroimaging, 22(4):365-374. [19] Oishi K, Lyketsos CG (2014). Alzheimer's disease and the fornix. Front Aging Neurosci, 11;6:24. [20] Deeb W, Salvato B, Almeida L, Foote KD, Amaral R, Germann J, et al. (2019). Fornix-Region Deep Brain Stimulation-Induced Memory Flashbacks in Alzheimer's Disease. N Engl J Med, 381(8):783-785. [21] Li R, Zhang C, Rao Y, Yuan TF (2022). Deep brain stimulation of fornix for memory improvement in Alzheimer's disease: A critical review. Ageing Res Rev, 79:101668. [22] Glasser MF, Van Essen DC (2011). Mapping human cortical areas in vivo based on myelin content as revealed by T1- and T2-weighted MRI. J Neurosci, 31(32):11597-11616. [23] Zhang H, Schneider T, Wheeler-Kingshott CA, Alexander DC (2012). NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. Neuroimage, 61(4):1000-1016. [24] Fukutomi H, Glasser MF, Murata K, Akasaka T, Fujimoto K, Yamamoto T et al. (2019). Diffusion Tensor Model links to Neurite Orientation Dispersion and Density Imaging at high b-value in Cerebral Cortical Gray Matter. Sci Rep, 9(1):12246. [25] Behrens TE, Berg HJ, Jbabdi S, Rushworth MF, Woolrich MW (2007). Probabilistic diffusion tractography with multiple fibre orientations: What can we gain? Neuroimage, 34(1):144-155. [26] Fieremans E, Jensen JH, Helpern JA (2011). White matter characterization with diffusional kurtosis imaging. Neuroimage, 58(1):177-188. [27] Scoville WB, Milner B (1957). Loss of recent memory after bilateral hippocampal lesions. J Neurol Neurosurg Psychiatry 20:11–21. [28] Mishkin M (1978). Memory in monkeys severely impaired by combined but not by separate removal of amygdala and hippocampus. Nature, 273, 297-298. [29] Parker A, Gaffan D (1997). Mamillary body lesions in monkeys impair object-in-place memory: Functional unity of the fornix-mamillary system. J. Cognitive Neurosci., 9, 512-521. [30] Aggleton JP, Pearce JM (2002). Neural systems underlying episodic memory: insights from animal research. In: Baddeley, A., Aggleton, J. P. and Conway, M. eds. Episodic Memory: New Directions in Research. Oxford: Oxford University Press, pp. 204-231. [31] Aggleton JP, Brown MW (2006). Interleaving brain systems for episodic and recognition memory. Trends Cogn Sci, 10(10), 455-463. [32] Aggleton JP, Wright NF, Rosene DL, Saunders RC (2015). Complementary patterns of direct amygdala and hippocampal projections to the macaque prefrontal cortex. Cereb Cortex, 25:4351–4373. [33] Buckley MJ, Mitchell AS. (2016). Retrosplenial cortical contributions to anterograde and retrograde memory in the monkey. Cereb Cortex, 26:2905-2918. [34] Passingham R (2009). How good is the macaque monkey model of the human brain? Curr Opin Neurobiol, 19(1):6-11. [35] Phillips KA, Bales KL, Capitanio JP, Conley A, Czoty PW, Hart BA et al. (2014). Why primate models matter. Am J Primatol, 76(9):801-827. [36] Mitchell AS, Thiele A, Petkov CI, Roberts A, Robbins TW, Schultz W, Lemon R (2018). Continued need for non-human primate neuroscience research. Curr Biol, 28(20):R1186-R1187. [37] Mars RB, Neubert FX, Verhagen L, Sallet J, Miller KL, Dunbar RI, Barton RA (2014). Primate comparative neuroscience using magnetic resonance imaging: promises and challenges. Front Neurosci, 8:298. [38] Mars RB, Foxley S, Verhagen L, Jbabdi S, Sallet J, Noonan MP et al. (2016). The extreme capsule fiber complex in humans and macaque monkeys: a comparative diffusion MRI tractography study. Brain Struct Funct, 221(8):4059-4071. [39] Mars RB, Sotiropoulos SN, Passingham RE, Sallet J, Verhagen L, Khrapitchev AA et al. (2018). Whole brain comparative anatomy using connectivity blueprints. Elife. 11;7:e35237. [40] Warrington S, Bryant KL, Khrapitchev AA, Sallet J, Charquero-Ballester M, Douaud G et al. (2020). XTRACT - Standardised protocols for automated tractography in the human and macaque brain. Neuroimage, 217:116923. |
| Additional Investigators | |
| Investigator's Name: | Anna Mitchell |
| Proposed Analysis: | Neuroimaging data can provide unique ways to probe the brain tissue microstructure and brain network connections. With the present application, we request access to the valuable ADNI neuroimaging resource so that we may compare the brain integrity and connectivity changes in people with mild cognitive impairment (MCI) and Alzheimer’s disease (AD) with the changes that emerge after selective fornix damage in non-human primates (NHP). In our research with NHPs, we have conducted neuroimaging in the macaque monkey animal model combined with behavioral and cognitive assessments on learning and memory tasks before and after a fornix transection in each animal. A separate age and cohort matched group of NHPs remained neurologically intact. We have collected T1-weighted and diffusion-weighted MRI, as well as resting-state functional MRI data from all animals to investigate structural and functional changes in the primate brain caused by fornix transection. In our latest experiments, we used 2 groups of NHPs: the first group (experimental) participated in behavioral training to learn a variant of the object-in-place scene discrimination task (OIP; [1]) that assays episodic-like memory in monkeys [2]. Subsequently, the experimental group underwent neurosurgery to receive a selective bilateral fornix transection. The second group (control) were trained on a passive visual fixation task [3], and operated under general anaesthesia to receive a head-post implanted onto the skull -but remained neurologically intact. We collected MRI data from all monkeys at various time points during the course of the experiments, both pre-operatively (before training, and at key intervals after training started) and post-operatively (at key time points following post-operative recovery). In a recent article from our group [4], we showed that, compared to the control group, training on the OIP visuo-spatial memory task induced alterations in the uncinate fasciculus (UF), fornix, and the ventral prefrontal white matter tracts, as well as functional connectivity changes within and between key fronto-temporal grey matter regions. Furthermore, we found that disrupting neural communication via the fornix caused marked changes to the training-related functional and structural profile, as well as impaired learning of new information. More recently [5], we focused on the fornix-transection-related alterations in structural connectivity quantified by probabilistic tractography. We employed the number of tracking streamlines to probe connectivity changes in the experimental group compared to the control across our regions of interest (ROIs). In the neurologically intact monkeys, we observed high connectivity across certain ROIs, including the CA3 hippocampal subfield with the retrosplenial cortex (RSC), the anterior thalamus with the RSC, as well as the RSC with the anterior cingulate cortex (ACC). However, compared to the control group, the fornix-transected monkeys showed significant connectivity changes including increases between the anterior thalamus and the ACC, and between the CA3 and the ACC, as well as decreases between the CA3 and the RSC. Building up on our previous research and expertise in neuroimaging, as well as on our experience in conducting analyses on large scale human neuroimaging datasets [UK Biobank and the Human Connectome Project (Pelekanos et al., in preparation -two papers)], in the present study we will aim to understand the associations between AD neuropathology and brain structural integrity and connectivity as depicted by imaging signatures in the ADNI, and the integrity and connectivity measurements in our NHP neuroimaging dataset. To this end, we will investigate the T1-weighted and diffusion-weighted images in both the humans and the NHPs to evaluate whether a common profile of grey matter integrity and connectivity alterations take place in AD pathology and after fornix damage. Our hypothesis is that fornix transection in the macaque and the brain pathologies in AD are likely to cause many similar changes in brain structures and networks that support normal memory function (such as the medial temporal lobes, the frontal, cingulate, and retrosplenial cortices, and the medial diencephalon). Evidence to support this hypothesis stems from the fact that damage to the fornix in both NHPs and humans has been consistently shown to cause deficits in learning and memory, and can produce anterograde amnesia [1, 6-12]. Furthermore, human MRI studies have indicated that the fornix is associated with recollection memory and recall in adults [13-15], and with episodic memory in childhood [16]. Last but not least, the fornix’s functional and anatomical features have been suggested as potential prognostic biomarkers for AD: the microstructural integrity of the fornix has been indicated as a predictive marker of memory decline [17-19], while recent clinical trials revealed that deep brain stimulation of the fornix improved cognitive functions in small groups of patients with Alzheimer’s disease [20; for a recent review see ref. 21]. We will assess grey matter integrity in both species using multi-modal MRI analysis to evaluate (i) cortical thickness, derived from the T1-weighted images, corrected for B1- inhomogeneity, (ii) intracortical myelin content, based on the same T1-weighted images [22] as well as (iii) grey-matter neurite orientation dispersion (OD) and neurite density (ND), derived from the diffusion-weighted dataset [23]. The different measures would be expected to reflect different, but related, aspects of grey matter integrity and structure [24] and will thus be analysed using multivariate tools. Grey-matter degeneration is likely related with degeneration of associated white matter tracts. To test this, we will also measure white-matter microstructural changes in tracts like the uncinate fasciculus using crossing-fibre probabilistic tractography [25] based on the diffusion-weighted images. Using the tractography results, we will extract microstructural properties, including DTI measures and higher-order metrics, such as neurite density and extracellular volume fraction from the NODDI model [23], as well as diffusional kurtosis imaging [26]. Finally, based on the diffusion-weighted images, we will also employ the tractography streamlines seeded from a range of grey matter ROIs to quantify structural connectivity changes ROI-by-ROI. We will use the same homologous ROIs in both species, selected on the basis of the ROIs’ role in higher cognition and memory. Specifically, we will test ROIs in the anterior and medial temporal lobes, the retrosplenial, cingulate and frontal cortex, and the medial diencephalon, that have been consistently shown to support memory functions in both humans and NHPs [27-33]. Given the fornix’s prominence in the limbic circuit and importance for memory, as well as the macaque NHPs’ similarity to humans in terms of physiology, neuroanatomy, and cognition, and the importance of involving the NHP model in biomedical research [e.g., 34-36], we are confident that comparing brain changes in our NHP model with those in human AD and/or MCI patients will provide further understanding about the neural degeneration associated with memory disorders. Comparative neuroimaging studies in humans and macaques have shown that diffusion MRI, in particular, can capture neuroanatomical similarities and differences between the two species [37-38], while more recent studies have developed methodology and frameworks that facilitate the understanding of similarities and differences between the brains of primates [39-40]. As a final note, we would be grateful if, apart from the raw and pre-processed MRI data (including structural, functional and diffusion-weighted modalities), we could also get access to the PET imaging dataset, as we are also planning to investigate the possible relationship between tau and Amyloid-β neuropathologies with cortical degeneration. Thank you very much for considering our application. Vassilis Pelekanos and Anna S. Mitchell References [1] Gaffan D (1994). Scene-specific memory for objects: A model of episodic memory impairment in monkeys with fornix transection. J Cogn Neurosci, 6:305–320. [2] Murray EA, Wise SP (2010). What, if anything, can monkeys tell us about human amnesia when they can’t say anything at all? Neuropsychologia, 48: 2385-2405. [3] Pelekanos V, Mok R, Joly O, Ainsworth M, Kyriazis D, Kelly M, Bell A, Kriegeskorte N (2020). Rapid event related, BOLD fMRI, non-human primates (NHP): choose two out of three. Sci Rep, 4;10(1):7485. [4] Pelekanos V, Premereur E, Mitchell D, Chakraborty S, Mason S, Lee A, Mitchell AS (2020). Cortico-cortical and thalamocortical changes in functional connectivity and white matter structural integrity after reward-guided learning of visuospatial discriminations in rhesus monkeys. J Neurosci, 40(41):7887-7901. [5] Pelekanos V, Premereur E, Mitchell AS. (In Press). Structural connectivity changes after fornix transection in macaques using probabilistic diffusion tractography. Advances in Experimental Medicine and Biology. [6] Gaffan D, Gaffan EA (1991). Amnesia in man following transection of the fornix. Brain, 114:2611-2618. [7] D’Esposito M, Verfaellie M, Alexander MP, Katz DI. (1995). Amnesia following traumatic bilateral fornix transection. Neurology, 45: 1546–1550. [8] Aggleton JP, McMackin D, Carpenter K, Hornak J, Kapur N, Halpin S et al. (2000). Differential cognitive effects of colloid cysts in the third ventricle that spare or compromise the fornix. Brain, 123 (Pt 4):800–815. [9] Charles DP, Gaffan D, Buckley MJ (2004). Impaired recency judgments and intact novelty judgments after fornix transection in monkeys. J Neurosci, 24:2037-2044. [10] Poreh A, Winocur G, Moscovitch M, Backon M, Goshen E, Ram Z, Feldman Z (2006). Anterograde and retrograde amnesia in a person with bilateral fornix lesions following removal of a colloid cyst. Neuropsychologia, 44:2241–2248. [11] Kwok SC, Mitchell AS, Buckley MJ (2015). Adaptability to changes in temporal structure is fornix-dependent. Learn Mem, 22:354-359. [12] Wilson CR, Charles DP, Buckley MJ, Gaffan D (2007). Fornix transection impairs learning of randomly changing object discriminations. J Neurosci, 27:12868-12873. [13] Tsivilis D, Vann SD, Denby C, Roberts N, Mayes AR, Montaldi D, Aggleton JP (2008). A disproportionate role for the fornix and mammillary bodies in recall versus recognition memory. Nat Neurosci, 11(7), 834-842. [14] Rudebeck SR, Scholz J, Millington R, Rohenkohl G, Johansen-Berg H, Lee AC (2009). Fornix microstructure correlates with recollection but not familiarity memory. J Neurosci, 29(47):14987-92. [15] Metzler-Baddeley C, Jones DK, Belaroussi B, Aggleton JP, O'Sullivan MJ (2011). Frontotemporal connections in episodic memory and aging: a diffusion MRI tractography study. J Neurosci, 31(37):13236-13245. [16] Hoffman LJ, Ngo CT, Canada KL, Pasternak O, Zhang F, Riggins T, Olson IR (2022). The fornix supports episodic memory during childhood. Cereb Cortex, bhac022. [17] Mielke MM, Okonkwo OC, Oishi K, Mori S, Tighe S, Miller MI et al. (2012). Fornix integrity and hippocampal volume predict memory decline and progression to Alzheimer's disease. Alzheimers Dement, 8(2):105-113. [18] Oishi K, Mielke MM, Albert M, Lyketsos CG, Mori S (2012). The fornix sign: a potential sign for Alzheimer's disease based on diffusion tensor imaging. J Neuroimaging, 22(4):365-374. [19] Oishi K, Lyketsos CG (2014). Alzheimer's disease and the fornix. Front Aging Neurosci, 11;6:24. [20] Deeb W, Salvato B, Almeida L, Foote KD, Amaral R, Germann J, et al. (2019). Fornix-Region Deep Brain Stimulation-Induced Memory Flashbacks in Alzheimer's Disease. N Engl J Med, 381(8):783-785. [21] Li R, Zhang C, Rao Y, Yuan TF (2022). Deep brain stimulation of fornix for memory improvement in Alzheimer's disease: A critical review. Ageing Res Rev, 79:101668. [22] Glasser MF, Van Essen DC (2011). Mapping human cortical areas in vivo based on myelin content as revealed by T1- and T2-weighted MRI. J Neurosci, 31(32):11597-11616. [23] Zhang H, Schneider T, Wheeler-Kingshott CA, Alexander DC (2012). NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. Neuroimage, 61(4):1000-1016. [24] Fukutomi H, Glasser MF, Murata K, Akasaka T, Fujimoto K, Yamamoto T et al. (2019). Diffusion Tensor Model links to Neurite Orientation Dispersion and Density Imaging at high b-value in Cerebral Cortical Gray Matter. Sci Rep, 9(1):12246. [25] Behrens TE, Berg HJ, Jbabdi S, Rushworth MF, Woolrich MW (2007). Probabilistic diffusion tractography with multiple fibre orientations: What can we gain? Neuroimage, 34(1):144-155. [26] Fieremans E, Jensen JH, Helpern JA (2011). White matter characterization with diffusional kurtosis imaging. Neuroimage, 58(1):177-188. [27] Scoville WB, Milner B (1957). Loss of recent memory after bilateral hippocampal lesions. J Neurol Neurosurg Psychiatry 20:11–21. [28] Mishkin M (1978). Memory in monkeys severely impaired by combined but not by separate removal of amygdala and hippocampus. Nature, 273, 297-298. [29] Parker A, Gaffan D (1997). Mamillary body lesions in monkeys impair object-in-place memory: Functional unity of the fornix-mamillary system. J. Cognitive Neurosci., 9, 512-521. [30] Aggleton JP, Pearce JM (2002). Neural systems underlying episodic memory: insights from animal research. In: Baddeley, A., Aggleton, J. P. and Conway, M. eds. Episodic Memory: New Directions in Research. Oxford: Oxford University Press, pp. 204-231. [31] Aggleton JP, Brown MW (2006). Interleaving brain systems for episodic and recognition memory. Trends Cogn Sci, 10(10), 455-463. [32] Aggleton JP, Wright NF, Rosene DL, Saunders RC (2015). Complementary patterns of direct amygdala and hippocampal projections to the macaque prefrontal cortex. Cereb Cortex, 25:4351–4373. [33] Buckley MJ, Mitchell AS. (2016). Retrosplenial cortical contributions to anterograde and retrograde memory in the monkey. Cereb Cortex, 26:2905-2918. [34] Passingham R (2009). How good is the macaque monkey model of the human brain? Curr Opin Neurobiol, 19(1):6-11. [35] Phillips KA, Bales KL, Capitanio JP, Conley A, Czoty PW, Hart BA et al. (2014). Why primate models matter. Am J Primatol, 76(9):801-827. [36] Mitchell AS, Thiele A, Petkov CI, Roberts A, Robbins TW, Schultz W, Lemon R (2018). Continued need for non-human primate neuroscience research. Curr Biol, 28(20):R1186-R1187. [37] Mars RB, Neubert FX, Verhagen L, Sallet J, Miller KL, Dunbar RI, Barton RA (2014). Primate comparative neuroscience using magnetic resonance imaging: promises and challenges. Front Neurosci, 8:298. [38] Mars RB, Foxley S, Verhagen L, Jbabdi S, Sallet J, Noonan MP et al. (2016). The extreme capsule fiber complex in humans and macaque monkeys: a comparative diffusion MRI tractography study. Brain Struct Funct, 221(8):4059-4071. [39] Mars RB, Sotiropoulos SN, Passingham RE, Sallet J, Verhagen L, Khrapitchev AA et al. (2018). Whole brain comparative anatomy using connectivity blueprints. Elife. 11;7:e35237. [40] Warrington S, Bryant KL, Khrapitchev AA, Sallet J, Charquero-Ballester M, Douaud G et al. (2020). XTRACT - Standardised protocols for automated tractography in the human and macaque brain. Neuroimage, 217:116923. |
