Skip to main content
Download PDF

Comparing a pre-defined versus deep learning approach for extracting brain atrophy patterns to predict cognitive decline due to Alzheimer’s disease in patients with mild cognitive symptoms

Alzheimer's Research & Therapy volume 16, Article number: 61 (2024) Cite this article

Abstract

Background

Predicting future Alzheimer’s disease (AD)-related cognitive decline among individuals with subjective cognitive decline (SCD) or mild cognitive impairment (MCI) is an important task for healthcare. Structural brain imaging as measured by magnetic resonance imaging (MRI) could potentially contribute when making such predictions. It is unclear if the predictive performance of MRI can be improved using entire brain images in deep learning (DL) models compared to using pre-defined brain regions.

Methods

A cohort of 332 individuals with SCD/MCI were included from the Swedish BioFINDER-1 study. The goal was to predict longitudinal SCD/MCI-to-AD dementia progression and change in Mini-Mental State Examination (MMSE) over four years. Four models were evaluated using different predictors: (1) clinical data only, including demographics, cognitive tests and APOE ε4 status, (2) clinical data plus hippocampal volume, (3) clinical data plus all regional MRI gray matter volumes (N = 68) extracted using FreeSurfer software, (4) a DL model trained using multi-task learning with MRI images, Jacobian determinant images and baseline cognition as input. A double cross-validation scheme, with five test folds and for each of those ten validation folds, was used. External evaluation was performed on part of the ADNI dataset, including 108 patients. Mann-Whitney U-test was used to determine statistically significant differences in performance, with p-values less than 0.05 considered significant.

Results

In the BioFINDER cohort, 109 patients (33%) progressed to AD dementia. The performance of the clinical data model for prediction of progression to AD dementia was area under the curve (AUC) = 0.85 and four-year cognitive decline was R2 = 0.14. The performance was improved for both outcomes when adding hippocampal volume (AUC = 0.86, R2 = 0.16). Adding FreeSurfer brain regions improved prediction of four-year cognitive decline but not progression to AD (AUC = 0.83, R2 = 0.17), while the DL model worsened the performance for both outcomes (AUC = 0.84, R2 = 0.08). A sensitivity analysis showed that the Jacobian determinant image was more informative than the MRI image, but that performance was maximized when both were included. In the external evaluation cohort from ADNI, 23 patients (21%) progressed to AD dementia. The results for predicted progression to AD dementia were similar to the results for the BioFINDER test data, while the performance for the cognitive decline was deteriorated.

Conclusions

The DL model did not significantly improve the prediction of clinical disease progression in AD, compared to regression models with a single pre-defined brain region.

Introduction

Alzheimer’s disease (AD) affects millions of individuals worldwide. AD dementia is characterized by a prolonged prodromal phase in which amyloid pathology accumulates in the brain before cognitive decline starts [1]. The subsequent onset of tau pathology and atrophy tracks more closely with occurrence of symptoms in the early phase of the disease in which individuals experience only subjective cognitive decline (SCD), or cognitive decline that qualify for mild cognitive impairment (MCI) [1, 2]. However, despite this understanding of the pathophysiological cascade of AD, it is not straight forward which individuals, who are present in a clinical setting with SCD/MCI, will progress to AD dementia (early AD) versus those that remain with stable SCD/MCI or develop other dementias (non-AD) [3,4,5]. It is also often unclear at which rate individuals with SCD/MCI will continue to decline cognitively, particularly due to varying biological resiliency to AD pathology at the individual level [6]. With the recent breakthroughs in disease-modifying treatments (DMT) against AD [7, 8], it may be possible to alter the course of disease in patients with SCD/MCI due to AD. Given the heterogeneity of the SCD/MCI population, it is now urgent to bring forward methods that can guide physicians when making decisions about which patients that are most likely to benefit from receiving DMTs targeting AD pathology.

To improve the prognosis of AD-related cognitive changes, artificial intelligence (AI) could be useful. Features from clinical data and different biomarker modalities can automatically be extracted and combined, to guide in the discrimination between individuals who will remain with SCD/MCI and those who will be diagnosed with AD dementia. One of the most common imaging modalities used for this task is magnetic resonance imaging (MRI). Several previous studies have utilized AI and MRI for this, including for example [9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24]. As can be seen in the review by Grueso et al. [25], the AI methods used vary but often include the support vector machine. However, in more recent studies the usage of deep learning (DL) and convolutional neural networks (CNN) have become frequent thanks to larger datasets and increased computational power. With DL it is possible to extract complex features from a large amount of data, such as a 3D MRI image. A review of publications regarding AD dementia detection using DL is given in [26], showing that MRI is the most widely available and used biomarker for this task, but a variety of DL models are used, including both voxel-based, slice-based, patch-based and region of interest-based. Despite this development in methods, there is only a few studies [14, 22] that objectively compare the performance of AI methods with more intuitive methods, such as logistic or linear regression models with a restricted number of predictors of cognitive decline in the early stages of AD. In SCD/MCI patients, there is also a lack of studies for prediction of longitudinal cognitive decline using commonly used continuous measures of cognition, rather than progression to AD dementia as an outcome.

The main goal of the present study was to perform an unbiased comparison between models utilizing different baseline information for prediction of future cognitive decline in SCD/MCI patients. This is done to understand which variables hold the most information and which method is the most accurate. Two different predictions were done – progression from SCD/MCI to AD dementia within four years (binary outcome) and four-years Mini Mental State Examination (MMSE) slope (continuous outcome). The variables evaluated were demographic information, baseline cognition based on cognitive tests, APOE genotype, predefined volumetric variables, and MRI images. The models used were logistic and linear regression, random forest as well as a DL model consisting of a three-dimensional (3D) CNN. Understanding and evaluating the clinical usefulness of these models have rarely been done in an independent and prospective manner. The present study aims to answer whether most of the prognostic information about cognitive decline in an MRI scan is contained in features of the brain (1) which can be obtained through volumetric analysis of pre-specified brain regions, or (2) which can only be obtained through an AI model identifying novel and previously unspecified patterns of brain structure. In this sense, the present study can provide insight as to which level of abstraction prognostic information for AD dementia progression is found.

Methods

Cohort description – BioFINDER

Individuals with SCD or MCI were included from the Swedish BioFINDER-1 study (clinical trial no. NCT01208675). The study protocol is described in detail before [27]. Briefly, the consecutively recruited participants in BioFINDER-1 are aged between 60 and 80 years, perform ≥ 24 points on the MMSE, and have been referred to any of the participating memory clinics due to cognitive complaints. A neuropsychological assessment including a comprehensive test battery was used to classify participants as SCD or MCI as previously described [28]. All patients with MCI were classified based on the DSM-5 criteria for MCI [29]. Note however, that for this study the SCD and MCI groups were analyzed together, since the aim of the project was to develop methods that would be useful for longitudinal predictions in an unselected group of patients with cognitive complaints, prior to dementia. Exclusion criteria were cognitive impairment that could be better accounted for by another non-neurodegenerative condition, severe somatic disease, and current alcohol or substance abuse. Only patients with available baseline MRI scans and longitudinal cognitive follow-up of at least four years from baseline were included here. Demographic information (age, sex, education), APOE ε4 carriership status (negative/positive) and baseline cognition (MMSE score and Alzheimer’s Disease Assessment Scale [ADAS] delayed word recall) were collected for all individuals.

To guarantee reproducibility and robustness of the models, a double cross-validation scheme was used, such that the data from all individuals could be utilized for both training, validation and test. A five-fold division into development set (80%, used for training and validation) and test set (20%) was used. A stratified random split was used, such that there was no significant difference between development and test sets in the distribution of diagnosis, age, education, sex, or APOE status. The stratified random split was produced by randomly splitting the dataset until a division fulfilling the constraints was obtained. For the development sets a 10-fold cross-validation was used, where the folds were drawn such that the same ratio of early AD and non-AD was obtained for each fold, but otherwise randomly. Similarly to the division into test and development, the stratified random split was produced by randomly splitting the dataset until a division fulfilling the constraints was obtained. The double cross-validation scheme was chosen to get robustness and good estimates of the uncertainties of the results. An overview of the demographics for the cohort can be seen in Table 1.

Cohort description – ADNI

A sub cohort of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset was used for independent evaluation. All participants included had MCI at baseline, at least four-year longitudinal cognitive follow-up from baseline and either MCI or AD dementia after four years. The same variables as included from the BioFINDER cohort where included, including MRI images. An overview of the demographics for the cohort can be seen in Table 1.

Table 1 Demographic of patients included in the study
Full size table

Study outcomes

There were two outcomes of interest. The primary binary outcome was four-year progression to AD dementia, where a clinical diagnosis of AD dementia during the four years follow-up was considered as a progression. Clinical status of AD dementia was evaluated according to the DSM-5 criteria for major neurocognitive disorders and recorded at each visit by a senior neuropsychologist and experienced memory disorder specialist. Additionally, a diagnosis of AD dementia was only used if the participant had an abnormal cerebrospinal fluid profile consistent with AD pathological change. The diagnostic process is described in detail in [27]. The primary continuous outcome was four-year cognitive decline as measured by the change from baseline in the MMSE score. MMSE is a measure of global cognition, and ranges from 0 to 30 where lower scores indicate worse cognition. Four-year cognitive decline was measured by fitting linear regression models for each individual separately using all available follow-up data within four years from baseline, then extracting the estimated regression slope.

MRI procedures

T1-weighted MRI was performed on a 3T Skyra MRI scanner (Siemens Healthineers, Erlangen, Germany) producing a high-resolution anatomical MP-RAGE image (TR = 1950 ms, TE = 3.4 ms, 1 mm isotropic voxels, 178 slices). The MRI images were minimally processed using skull stripping, bias correction, and normalization to MNI152 template space [30] using ANTS [31] (normalized using a multi-resolution level [shrink factors = 8, 4, 2, 1, and smoothing sigmas = 3, 2, 1, 0] approach with rigid [mutual information metric 32 bins, regular sampling 25%], affine [mutual information metric 32 bins, regular sampling 25%] and SyN [affine + deformable, cross correlation metric, search radius = 4, full sampling]). Cortical reconstruction and volumetric segmentation were performed with the FreeSurfer image analysis pipeline, as described previously [32]. The Jacobian determinant (JD) images where computed based on the anatomical MRIs non-linear warp to template space and quantify the local deformations, wherein reduced brain matter and atrophy are gauged. Thus, the MRI images are used in native space, as a normalization to MNI space could erase the crucial information, while the JD images are in MNI template space as they map out the expansion/contraction of voxels relative a normal brain and are expected to add some information to the MRI in the form of tissue loss and atrophy relative a normal template brain.

Sets of predictors

Several a priori measurements are related to change in cognition, wherefor we investigated several different types of data and models, see Fig. 1. The first model [“Clinical data model”] utilized readily available demographics information (age, sex, and education), MMSE score, ADAS delayed word recall [33] and APOE status. The second model [“Hippocampal volume model”], used hippocampal volume (average of left and right hemisphere) as well as intracranial volume added to the clinical data model. The third model [“FreeSurfer model”], used regional brain volumes from the FreeSurfer pipeline together with intracranial volume added to the clinical data model. The fourth model [“DL model”] used whole brain MRI and JD images along with the clinical data variables in a CNN model. The different models are described in more detail below.

Fig. 1
figure 1

Overview of study. The study consisted of three parts: participant selection, model fitting, and model evaluation

Full size image

Basic and Volumetric models

For the clinical data model and the hippocampal volume model we trained logistic regression models for prediction of progression to AD dementia and linear regression models for prediction of longitudinal cognitive decline. For the FreeSurfer models, random forest was used.

For all of the models the features were standardized by removing the mean and scaling to unit variance. The models were optimized using the Scikit-learn library (v. 0.22) in Python (v. 3.5) [34]. For the random forest models, the random forest classifier was used for prediction of progression to AD dementia and the random forest regressor was used for prediction of longitudinal cognitive decline, both with default parameters.

Deep learning models

CNNs work by learning a successively more complex representation of images across its increasing layers, where the earliest layers closest to the input image are activated by simple shapes such as edges, followed by more complex structures. This method of creating an increasing complex visual representation is similar to how the brain’s visual cortex processes images. We used the CNN architecture suggested by Spasov et al. [14], which is a parameter-efficient network, reducing the risk of overfitting when using small datasets, and has previously been proven successful [13, 14]. We modified the network slightly for our settings, see Supplementary Fig. 1. The model utilizes both the MRI image, the JD image and the clinical data. The main modification done compared to [14] was to train the network for new tasks using multi-task learning [35], which reduces the risk of overfitting the model.

When using multi-task learning the network is trained for several tasks simultaneously, which for example can be beneficial when the dataset used is limited in size and thus the risk of overtraining is high. The multi-task learning was implemented by using three output layers, one for each of the tasks (i) discrimination for four-year progression to AD with sigmoid activation and class weighted categorical cross-entropy loss \({L}_{1}\), (ii) prediction of four-year cognitive decline measured with MMSE slope with linear activation and mean-squared-error loss \({L}_{2}\), and (iii) prediction of hippocampal volume with linear activation and mean-squared-error loss \({L}_{3}\). Thus, each training example was used for all three tasks and the total loss function \(L\) was a weighted sum of the three individual ones with weights \({w}_{1},{w}_{2},{w}_{3}\),

$$L={w}_{1}{L}_{1}+{w}_{2}{L}_{2}+{w}_{3}{L}_{3} .$$
(1)

Depending on which task was the main one, the weighting of the different individual losses was modified. For the model discriminating four-year progression to AD, we used \({w}_{1}=1\) and \({w}_{2}={w}_{3}=0.025\) which was found being a good configuration by testing values in the range 0-0.1, see Supplementary Table 1. Similarly, we used \({w}_{2}=1\) and \({w}_{1}={w}_{3}=0.025\) for the model predicting MMSE slope, see Supplementary Table 2.

The size of our MRI images and JDs differed slightly from the sizes used in the work by Spasov et al. [14]. To be able to use the same network architecture, the MRIs were cropped, and the JDs were padded with zeros. The MRI images were rescaled to have voxel values in range [-1, 1], by dividing by 0.5 times the largest voxel value in the entire MRI set and subtracting 1. No normalization of the JD images was done. The clinical data was all individually rescaled to have values in range [0,1], by removing the minimum value and dividing by the difference between the maximum and minimum value. The normalization techniques were based on the ones used in [14], but slightly modified based on performance on validation data.

Similar to the settings used in [14], the network was trained for 50 epochs using the Adam optimizer with the same learning rate scheduler. The model was implemented based on the code provided by [14] but with the final layers modified to be able to use multi-task learning. The implementation was done in Python (version 3.8) using the Keras library [36] with TensorFlow [37] as backend and trained on a Nvidia Tesla V100 graphics card with 32GB VRAM.

Statistical analysis

The primary analysis involved the models described above. A sensitivity analysis was also performed looking at the effect of including the MRI image only, the JD image only, or both in the DL model. Due to the training of each DL model taking several days, only one test fold was used in the sensitivity analysis. To improve model interpretability, canonical patterns of brain atrophy for the DL model were identified. Brain atrophy patterns are individualized in nature, so the block occlusion method was followed whereby parts of the image were systematically set to 0 and model performance was evaluated with these images and compared to images without any occlusion. Performing this procedure by systematically blacking out all parts of the image at different trials results in a whole brain atrophy pattern where it is assumed that regions whose blacking out results in a large decrease in model performance must be important to the DL model [38, 39]. This was done for five non-AD and five early AD individuals. The average of the results from the ten folds and ten individuals were used for the final illustrations.

The performance metric of interest for the binary outcome of four-year progression to AD dementia was area under the receiver operating characteristics curve (AUC) and Matthews correlation coefficient (MCC). For the continuous outcome of four-year cognitive decline, the performance metric of interest was R2.

The model fitting procedure involved first performing 10-fold cross validation on the development set, where the training set was the part of the data used to determine model parameters and the validation set was the part of the development set which was held out during cross validation to evaluate model parameters without looking at the test set. Once model parameters were determined from this internal cross validation procedure, performance was finally evaluated on the previously unseen test set and reported as the mean for the ten folds. Five different BioFINDER test sets were used, using a double cross-validation scheme. External evaluation on ADNI data was performed after all models had been finalized.

Statistically significant differences in demographics were determined using p-values from Fisher’s exact test (sex) or t-test for independent samples (remaining variables). To determine statistically significant differences in performance on the test sets for the different models, the results from the 10 folds were used in a Mann-Whitney U-test using the Scipy library (v. 1.4.1) in Python (v. 3.5). All p-values less than 0.05 were considered significant. Bootstrapping on the test sets was used to estimate 95% confidence intervals (CI).

Results

Cohort characteristics

A total of 332 participants from the Swedish BioFINDER-1 study were included in the present analysis, whereof 223 participants were non-AD participants who were cognitive stable with SCD or MCI for at least four years and the remaining 109 participants were early AD participants with SCD or MCI at baseline who subsequently progressed to AD dementia. As external validation, 108 participants from the ADNI cohort with MCI at baseline were included, whereof 85 participants remained with MCI for at least four years and the remaining 23 participants progressed to AD dementia.

In the BioFINDER cohort, the non-AD participants did not differ from early AD participants significantly on education (12.0 years vs. 11.8 years average, p-value 0.72), sex (47.5% female vs. 46.8% female, p-value 0.91) or intracranial volume (1136 cm3 vs. 1114 cm3 average, p-value 0.13), but the early AD group had a higher ratio of MCI (71.6% MCI vs. 41.2% MCI, p-value < 0.001), was older (72.1 years vs. 70.2 years average, p-value < 0.05), had lower baseline MMSE (27.1 vs. 28.2 average, p-value < 0.001), higher APOE ε4 allele presence (1.0 vs. 0.4, p-value < 0.001), worse ADAS score (6.7 vs. 4.2 average, p-value < 0.001) and smaller hippocampal volume (2927 mm3 vs. 3339 mm3 average, p-value < 0.001). The same trends could be seen in the ADNI cohort, except that all included participants were MCI.

Performance for predicting four-year progression to AD

The clinical data model consisting of demographics (age, sex, and education), MMSE score, ADAS delayed word recall and APOE status had a mean BioFINDER test AUC of 0.850 (CI 0.698–0.954) and MCC of 0.621 (CI 0.427–0.844) for predicting four-year progression to AD dementia. Adding intracranial and hippocampal volume to the clinical data model increased the test AUC to 0.862 (CI 0.728–0.960) and MCC to 0.623 (CI 0.450–0.848). Conversely, adding the FreeSurfer brain region volumes to the clinical data model, the test AUC decreased to 0.832 (CI 0.710–0.929) and MCC to 0.563 (CI 0.416–0.788). Adding whole brain MRI and JD images to the clinical data together in a CNN model led to a decreased test AUC of 0.840 (CI 0.688–0.957) and MCC of 0.605 (CI 0.418 − 0.850). Also, when testing if the differences of the four main models’ performances on the test sets are statistically significant, based on the 50 models from the 5 test sets and 10-fold cross-validation, it was found that the hippocampal volume model was significantly better (p-value < 0.05) than both the FreeSurfer and DL models, the Clinical data model was significantly better than the FreeSurfer model, while there was no significant difference between any other pair of models. The results for the external ADNI test data had no significant difference in performance from the BioFINDER test data except for the FreeSurfer model and DL model, which performed significantly worse (FreeSurfer: AUC 0.688, CI 0.536–0.827, DL: AUC 0.799, CI 0.677–0.904). The results are visualized in Figs. 2 and 3 and presented fully in Supplementary Table 1.

Fig. 2
figure 2

Box plots for AUC when predicting progression into AD dementia within four years. Results are for the BioFINDER test sets using the different models from double cross-validation. The clinical data includes demographics (age, sex, and education), baseline cognition (MMSE score, ADAS delayed word recall) and APOE genotype. The orange lines show the median, the boxes span from the first quartile to the third quartile, and the whiskers extend from the box by 1.5 times the interquartile range. Significant differences (p-value < 0.05) are indicated with *

Full size image
Fig. 3
figure 3

ROC curves and AUC for progression to AD dementia. Models using a) clinical data (age, sex, and education, baseline cognition [MMSE score, ADAS delayed word recall] and APOE genotype), b) clinical data, intracranial volume and hippocampal volume, c) clinical data, intracranial volume and FreeSurfer, d) clinical data, MRI and JD. The blue curve shows the results on the BioFINDER test data and the red curves the results on the ADNI data. The solid line presents the mean from the five test folds and ten cross-validation folds, the transparent lines present the induvial results, and the shaded areas show the mean plus/minus one standard deviation

Full size image

Sensitivity analysis of image modalities and multi-task learning included in deep learning model

The DL model described above included by default both the MRI image and the JD image derived from the image registration procedure. However, extracting JD images represents an extra, more burdensome processing step so we performed a sensitivity analysis in which the effect of fitting the DL model with MRI only or JD only. We found that using the JD image only, which had a mean test AUC of 0.605 (CI 0.418–0.766) for predicting four-year progression to AD dementia, outperformed a model using the MRI image only, which had a mean test AUC of 0.575 (CI 0.274–0.782). Moreover, we found that including both MRI and JD images had a mean test AUC of 0.609 (CI 0.366–0.766), thereby improving on the result from using the JD image only. The multi-task learning approach used was only evaluated for a limited number of \(w\)-values due to limited computational resources: \(w=\text{0,0.025,0.050,0.075,0.01}\). The best value was determined based on the validation data, found to be \({w}_{1}=1,{w}_{2}={w}_{3}=0.025\). All these results are displayed in Supplementary Table 1.

Performance for predicting four-year decline in cognition

The clinical data model consisting of demographics (age, sex, and education), baseline cognition (MMSE score, ADAS delayed word recall) and APOE status had a mean BioFINDER test R2 of 0.138 (CI -0.171–0.328) for predicting four-year cognitive decline as measured by MMSE. Adding hippocampal volume to the clinical data model improved the mean test R2 to 0.157 (CI -0.296–0.403). Adding FreeSurfer brain regions to the clinical data model improved the mean test R2 to 0.175 (CI -0.127–0.396). The DL model featuring the clinical data features and the whole brain MRI and JD images had the lowest mean test R2 of 0.079 (-0.206–0.267). The values for \(w\) that were found optimal for the previous task was used here as well but altered to prioritize this task (\({w}_{2}=1,{w}_{1}={w}_{3}=0.025\)). All models were significantly better (p-value < 0.05) than the DL model, and the hippocampal volume model was furthermore also significantly better than the clinical data model. However, the performance on the external ADNI test data was significantly worse for all models except the DL model. The results are visualized in Figs. 4 and 5 and presented fully in Supplementary Table 2.

Fig. 4
figure 4

Box plots for R2 when predicting four years MMSE slopes. Results are for the BioFINDER test sets using the different models from double cross-validation. The clinical data includes demographics (age, sex, and education), baseline cognition (MMSE score, ADAS delayed word recall) and APOE genotype. The orange lines show the median, the boxes span from the first quartile to the third quartile, and the whiskers extend from the box by 1.5 times the interquartile range. Significant differences (p-value < 0.05) are indicated with *

Full size image
Fig. 5
figure 5

Correlation between predicted slope and corresponding ground truth. Models using a) clinical data (age, sex, and education, baseline cognition [MMSE score, ADAS delayed word recall] and APOE genotype), b) clinical data, intracranial volume and hippocampal volume, c) clinical data, intracranial volume and FreeSurfer, d) clinical data, MRI and JD. The blue data shows the BioFINDER test sets and the red data shows the external evaluation on ADNI data

Full size image

Identifying atrophy patterns from FreeSurfer and deep learning models

Atrophy patterns representing the important brain regions of interest were identified for the DL whole brain model using the patch occlusion method described in the section “Statistical analysis”. Regions in the temporal and parietal lobes were identified, see visualization in Fig. 6.

Fig. 6
figure 6

Heatmap with features used by the CNN. This figure shows the results of an occlusion experiment. Areas of the brain which are colored red are those areas whose occlusion led to a drop in model performance compared to when no occlusion was used

Full size image

Discussion

We developed and evaluated different models for identifying SCD/MCI individuals who are more likely to progress to AD dementia within four years, as well as models for prediction of change in MMSE over four years. The models were based on combinations of demographics, standard cognitive tests, hippocampal volume, volumetric data from FreeSurfer, MRI 3D images and JDs computed from MRI. We focused on realistically evaluating model performances by following a rigorous approach without leakage of information from training to test sets as well as including external evaluation, and we also focused on unbiased comparisons between models of different complexity. In general, we found that a model with only demographics and baseline clinical data (cognitive tests plus APOE ε4 status) performed very well, and that only smaller improvements in predictive ability were seen when adding hippocampal volume or volumetrics data from FreeSurfer. We could not see consistent improvements in model performance when using the entire MR images in DL models. Taken together, this suggests that among the predictors tested here, most of the relevant predictive information for patients in the SCD/MCI stage of AD dementia is contained in the baseline cognitive profile together with an MRI assessment of hippocampal atrophy.

The problem of predicting progression to AD dementia has been studied multiple times before, both using MRI but also using data from e.g., PET. While PET has been shown to provide more information than MRI [25], it also has drawbacks such as being expensive and less available wherefore it was not used in this study. MRI in contrast is more readily available and a realistic option for prediction models in clinical practice. Previous publications have reported results with AUC values in the range 0.53–0.98 [25]. However, it is hard to compare the studies in a fair way due to variations in for example imaging modality (e.g., MRI or PET), prediction task (e.g., MCI versus AD, or progressive MCI versus stable MCI with different follow-up times), the dataset used as well as the dataset’s division into development and test data. Our best-performing model with an AUC of 0.86, on both the test data and the external evaluation data, is within the range of previous reported results. Many studies are using data from the publicly available ADNI cohort [40], but due to different exclusion criteria and validation methods the final ADNI datasets vary. Furthermore, according to the review in [12], comparing studies performing classification of AD dementia using DL and MRI, it was found that there may have been data leakage and thus a bias in the reported results in more than half of the surveyed papers. It was shown that when correctly dividing the data such that data from the same subject was never present in both training and test set simultaneously, the accuracy dropped from 99 to 90% [10]. We used data from the Swedish BioFINDER study, which has the major benefit of being a well-characterized cohort in terms of clinical diagnosis and biomarker confirmation of amyloid pathology as well as being more representative to a general memory clinic setting compared to the ADNI cohort (which is a more selected cohort, characterized for example by highly educated study participants, and few vascular co-pathologies). However, as external evaluation we include participants from the ADNI cohort. The test sets were only used for evaluation after all models were finalized, thus not in any way influencing the development of the algorithm or model selection.

Prediction of MMSE slope is a less explored task than prediction of AD dementia. Compared to the binary classification task of separating non-AD and early AD, it has the advantage of being a continuous measure which is potentially less prone to bias and subjectivity compared to a clinical diagnosis. Moreover, cognitive assessment is relevant because it is typically the primary endpoint for clinical trials of AD.

We investigated whether predefined volumetric variables, such as hippocampal volume and volumetric variables from FreeSurfer, or data-driven features from MRI using DL optimized for the given task, provided the most information for prediction of SCD/MCI-to-AD progression and MMSE slope. While the MRI ought to contain at least as much information as the volumetric variables, since the volumetric variables are determined from the MRI, we anyway obtain results showing that the hippocampal volume gives the best prediction for SCD/MCI-to-AD progression while the FreeSurfer variables give the best MMSE slope prediction. The reason for this is probably that the amount of data available for training is too small to learn more representative features using DL and that the model’s capacity is too limited. The limited capacity was chosen to avoid overfitting and define a model suitable to the amount of available data. The results show that more data is needed for DL models to be successful. Thus, the results should not be interpreted as a failure for DL, but rather that its success might require an order of magnitude more data. Furthermore, it has been established for a long time that pathological changes in AD are focused in the hippocampus [41]. A hypothesis for the difference in performance between the DL model and FreeSurfer model is that JDs derived from normalization can be difficult for a severely atrophied brain with very large ventricles. In such cases, native FreeSurfer segmentation could be more informative. However, the DL model also utilizes the MRIs in native space, so the worse performance of the DL model is probably caused by its limited capacity, chosen to fit the number of subjects available.

The DL model used in this work is based on the network developed by [14], which uses parameter-efficient layers such as grouped and separable convolutions. However, a few modifications were done to optimize it for our settings. Similar to the original network we used multi-task learning, but for other tasks. Instead of simultaneously predict SCD/MCI-to-AD progression as well as classify AD dementia vs. healthy controls using a dataset with normal, stable MCI and progressive MCI cases, we use a dataset with only SCD/MCI cases and predict SCD/MCI-to-AD progression together with four years MMSE slopes and hippocampal volume. We show that the multi-task learning approach improves the performance (Supplementary Table 1). Training the model for all tasks simultaneously has a regularizing effect on the training and reduces the risk of overfitting the model to the relatively small dataset. Furthermore, it is likely that different tasks find descriptive information in similar features, hence the multi-task learning should not limit the models’ capacities.

Our best-performing model for prediction of SCD/MCI-to-AD progression utilizes the clinical data together with the hippocampal volume. However, the clinical data model alone performs very well and, in most cases, outperforms the other models which only utilizes one type of data (hippocampal volume, FreeSurfer variables, MRI or JD), Supplementary Table 1. A similar result was obtained in the study by [14], where they obtained an AUC of 0.79 when using MRI only and an AUC of 0.88 when using clinical variables only, similarly to our clinical data model. However, in the study by [22], they received significantly better results using a DL model and MRI images compared to e.g. MMSE, hippocampal volume, cerebrospinal fluid Aβ and tau. They did however not evaluate using multiple simple features combined or looked at longitudinal cognitive decline using continuous measures of cognition, as we have done in this study.

While the atrophy patterns identified as important by the DL model differ for all patients, we can observe that the regions often coincide with regions that have been previously associated with AD dementia such as temporal and parietal lobes, see Fig. 6. The finding that the regions identified as important varied across patients is inherent to DL models compared to statistical models such as logistic regression that instead identify a common pattern of atrophy in the entire study population. The individualized nature of atrophy patterns derived from DL is a strong benefit because it can allow for personalized explanations as to how an individual’s predicted risk score was derived.

The main limitation of our study is the size of the dataset used, although it is similar in size (N = 332) to what has been used in some previous studies [ (11): N = 968, (14): N = 785, (15): N = 509, (16): N = 559, (20): N = 200, (21): N = 819, (22): N = 786] but of higher quality with fewer healthy controls and AD dementia and higher ratio of SCD/MCI patients [our: N = 332, (11): N = 413, (14): N = 409, (15): N = 210, (16): N = 216, (20): N = 97, (21): N = 398, (22): N = 582]. Another limitation is the selection of model architecture and hyperparameters, which can be further explored. We investigated different amount of multi-task learning (Supplementary Table 1) and different techniques for normalization of the input data, but further optimization could be done. However, the network architecture used has been optimized in previous studies using another dataset [13, 14], and by not optimizing it further we reduce the risk of overfitting it to our study. Finally, the evaluation is limited by the gold standard, which is determined by the cognition variables and thus is biased towards those. Thus, it is not surprising that these variables have high correlation with the outcomes we use. The clinical gold standard is used clinically today and there is no similar metric that is based on for example volumetric features. However, it could be that such a metric also has a high correlation with both patients’ symptoms as well as our MRI based models. Another aspect of our work is that we did not test for conversion to all-cause dementia. Our aim was to develop tools that specifically predicted development of dementia due to AD. Due to the recent breakthroughs in DMTs against AD [7, 8], it is of high importance to specifically predict future development of dementia due to AD rather than dementia due to other diagnoses, since only patients at risk for developing dementia due to AD should receive these new types of treatment.

Conclusions

We developed and evaluated four different models on two different outcomes. The models perform similar, but the clinical data model using only demographics (gender, age, education), baseline cognition (MMSE score, ADAS delayed word recall) and APOE status performs well, and only small improvements can be seen when adding hippocampal volume or regional MRI gray matter volumes extracted using FreeSurfer. For identification of patients with high risk of SCD/MCI-to-AD progression within four years we obtained an AUC of 0.862 and for four-years MMSE slope prediction we obtained an R2 score of 0.157 using clinical data and hippocampal volume. Similar results for prediction of SCD/MCI-to-AD progression was seen in the external ADNI cohort, while the results for predicting four-years MMSE slope was significantly deteriorated. The result for SCD/MCI-to-AD progression is similar to previous studies, while to the best of our knowledge no data are currently available with respect to prediction of MMSE slope. The best DL models identified uses multi-task learning, by being trained to simultaneously predict both SCD/MCI-to-AD progression, four years MMSE slope as well as hippocampal volume. We confirmed that the areas found as interesting by the DL models are reasonable using an occlusion algorithm. The results are humbling with respect to what can be achieved by DL models. In the future, it may be tested if better performance can be achieved by increasing the training sample size, or by adding additional investigational modalities or MRI-sequences, or by fine-tuning the outcome measures to minimize noise and variability.

Data availability

Anonymized data can be shared to qualified academic researchers after request for the purpose of replicating procedures and results presented in the study. Data transfer must be performed in agreement with EU legislation regarding general data protection regulation and decisions by the Ethical Review Board of Sweden and Region Skåne.

Abbreviations

AD:

Alzheimer’s disease

ADAS:

Alzheimer’s Disease Assessment Scale

ADNI:

Alzheimer’s Disease Neuroimaging Initiative

AI:

Artificial intelligence

AUC:

Area under the curve

CI:

Confidence intervals

CNN:

Convolutional neural network

DL:

Deep learning

DMT:

Disease–modifying treatments

JD:

Jacobian determinant

MCC:

Matthes correlation coefficient

MCI:

Mild cognitive impairment

MMSE:

Mini Mental State Examination

MRI:

Magnetic resonance imaging

SCD:

Subjective cognitive decline

References

  1. Hansson O. Biomarkers for neurodegenerative diseases. Nat Med. 2021;27(6):954–63.

    Article  CAS  PubMed  Google Scholar 

  2. Frisoni GB, Fox NC, Jack CR Jr, Scheltens P, Thompson PM. The clinical use of structural MRI in Alzheimer disease. Nat Reviews Neurol. 2010;6(2):67–77.

    Article  Google Scholar 

  3. Burnham SC, Loi SM, Doecke J, Fedyashov V, Dore V, Villemagne VL, Masters CL. The dawn of robust individualised risk models for dementia. Lancet Neurol. 2019;18(11):985–7.

    Article  PubMed  Google Scholar 

  4. Gaugler J, James B, Johnson T, Reimer J, Solis M, Weuve J, et al. 2022 Alzheimer’s disease facts and figures. Alzheimers Dement. 2022;18(4):700–89.

    Article  Google Scholar 

  5. Mitchell AJ, Shiri-Feshki M. Rate of progression of mild cognitive impairment to dementia–meta‐analysis of 41 robust inception cohort studies. Acta Psychiatrica Scandinavica. 2009;119(4):252–65.

    Article  CAS  PubMed  Google Scholar 

  6. Kumar A, Shoai M, Palmqvist S, Stomrud E, Hardy J, Mattsson-Carlgren N, Hansson O. Genetic effects on longitudinal cognitive decline during the early stages of Alzheimer’s disease. Sci Rep. 2021;11(1):19853.

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  7. Sims JR, Zimmer JA, Evans CD, Lu M, Ardayfio P, Sparks J, et al. Donanemab in early symptomatic Alzheimer disease: the TRAILBLAZER-ALZ 2 randomized clinical trial. JAMA. 2023;330(6):512–27.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Van Dyck CH, Swanson CJ, Aisen P, Bateman RJ, Chen C, Gee M, et al. Lecanemab in early Alzheimer’s disease. N Engl J Med. 2023;388(1):9–21.

    Article  PubMed  Google Scholar 

  9. Bae JB, Lee S, Jung W, Park S, Kim W, Oh H, et al. Identification of Alzheimer’s disease using a convolutional neural network model based on T1-weighted magnetic resonance imaging. Sci Rep. 2020;10(1):22252.

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  10. Bäckström K, Nazari M, Gu IY-H, Jakola AS. An efficient 3D deep convolutional network for Alzheimer’s disease diagnosis using MR images. 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018); 2018: IEEE.

  11. Zhang J, Zheng B, Gao A, Feng X, Liang D, Long X. A 3D densely connected convolution neural network with connection-wise attention mechanism for Alzheimer’s disease classification. Magn Reson Imaging. 2021;78:119–26.

    Article  PubMed  Google Scholar 

  12. Wen J, Thibeau-Sutre E, Diaz-Melo M, Samper-González J, Routier A, Bottani S, et al. Convolutional neural networks for classification of Alzheimer’s disease: overview and reproducible evaluation. Med Image Anal. 2020;63:101694.

    Article  PubMed  Google Scholar 

  13. Ramon-Julvez U, Hernandez M, Mayordomo E. Analysis of the influence of diffeomorphic normalization in the prediction of stable vs progressive MCI conversion with convolutional neural networks. 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI); 2020: IEEE.

  14. Spasov S, Passamonti L, Duggento A, Lio P, Toschi N. Alzheimer’s Disease Neuroimaging Initiative. A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer’s disease. NeuroImage. 2019;189:276–87.

    Article  PubMed  Google Scholar 

  15. Pan D, Zeng A, Jia L, Huang Y, Frizzell T, Song X. Early detection of Alzheimer’s disease using magnetic resonance imaging: a novel approach combining convolutional neural networks and ensemble learning. Front NeuroSci. 2020;14:259.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Hett K, Ta V-T, Oguz I, Manjón JV, Coupé P. Alzheimer’s Disease Neuroimaging Initiative. Multi-scale graph-based grading for Alzheimer’s disease prediction. Med Image Anal. 2021;67:101850.

    Article  PubMed  Google Scholar 

  17. Mofrad SA, Lundervold AJ, Vik A, Lundervold AS. Cognitive and MRI trajectories for prediction of Alzheimer’s disease. Sci Rep. 2021;11(1):2122.

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  18. Ocasio E, Duong TQ. Deep learning prediction of mild cognitive impairment conversion to Alzheimer’s disease at 3 years after diagnosis using longitudinal and whole-brain 3D MRI. PeerJ Comput Sci. 2021;7:e560.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Qiu S, Miller MI, Joshi PS, Lee JC, Xue C, Ni Y, et al. Multimodal deep learning for Alzheimer’s disease dementia assessment. Nat Commun. 2022;13(1):3404.

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  20. Chen X, Tang M, Liu A, Wei X. Diagnostic accuracy study of automated stratification of Alzheimer’s disease and mild cognitive impairment via deep learning based on MRI. Annals of Translational Medicine. 2022;10.14.

  21. Lim BY, Lai KW, Haiskin K, Kulathilake KA, Ong ZC, Hum YC, et al. Deep learning model for prediction of progressive mild cognitive impairment to Alzheimer’s disease using structural MRI. Front Aging Neurosci. 2022;14:876202.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Feng X, Provenzano FA, Small SA. Alzheimer’s Disease Neuroimaging Initiative. A deep learning MRI approach outperforms other biomarkers of prodromal Alzheimer’s disease. Alzheimers Res Ther. 2022;141:45.

    Article  Google Scholar 

  23. de Silva K, Kunz H. Prediction of Alzheimer’s disease from magnetic resonance imaging using a convolutional neural network. Intelligence-Based Med. 2023;7:100091.

    Article  Google Scholar 

  24. Romano MF, Zhou X, Balachandra AR, Jadick MF, Qiu S, Nijhawan DA et al. Deep learning for risk-based stratification of cognitively impaired individuals. iScience. 2023;26.9.

  25. Grueso S, Viejo-Sobera R. Machine learning methods for predicting progression from mild cognitive impairment to Alzheimer’s disease dementia: a systematic review. Alzheimers Res Ther. 2021;13:1–29.

    Article  Google Scholar 

  26. Ebrahimighahnavieh MA, Luo S, Chiong R. Deep learning to detect Alzheimer’s disease from neuroimaging: a systematic literature review. Comput Methods Programs Biomed. 2020;187:105242.

    Article  PubMed  Google Scholar 

  27. Palmqvist S, Tideman P, Cullen N, Zetterberg H, Blennow K, Alzheimer’s Disease Neuroimaging Initiative. Prediction of future Alzheimer’s disease dementia using plasma phospho-tau combined with other accessible measures. Nat Med. 2021;27(6):1034–42.

    Article  CAS  PubMed  Google Scholar 

  28. Mattsson N, Insel PS, Palmqvist S, Stomrud E, Van Westen D, Minthon L, et al. Increased amyloidogenic APP processing in APOE ɛ4-negative individuals with cerebral β-amyloidosis. Nat Commun. 2016;7(1):10918.

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  29. Petrazzuoli F, Vestberg S, Midlöv P, Thulesius H, Stomrud E, Palmqvist S. Brief cognitive tests used in primary care cannot accurately differentiate mild cognitive impairment from subjective cognitive decline. J Alzheimers Dis. 2020;75(4):1191–201.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Jenkinson M, Beckmann CF, Behrens TE, Woolrich MW, Smith SM. FSL NeuroImage. 2012;62(2):782–90.

    Article  PubMed  Google Scholar 

  31. Avants BB, Tustison NJ, Stauffer M, Song G, Wu B, Gee JC. The insight ToolKit image registration framework. Front Neuroinformatics. 2014;8:44.

    Article  Google Scholar 

  32. Mattsson N, Insel PS, Donohue M, Jögi J, Ossenkoppele R, Olsson T, et al. Predicting diagnosis and cognition with 18F-AV-1451 tau PET and structural MRI in Alzheimer’s disease. Alzheimer’s Dement. 2019;15(4):570–80.

    Article  Google Scholar 

  33. Rosen WG, Mohs RC, Davis KL. A new rating scale for Alzheimer’s disease. Am J Psychiatry. 1984;141(11):1356–64.

    Article  CAS  PubMed  Google Scholar 

  34. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: machine learning in Python. J Mach Learn Res. 2011;12:2825–30.

    MathSciNet  Google Scholar 

  35. Caruana R. Multitask learning. Mach Learn. 1997;28:41–75.

    Article  Google Scholar 

  36. Chollet F, Keras. 2015 [Available from: https://keras.io, accessed 6 October 2023.].

  37. Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean J et al. TensorFlow: a system for Large-Scale machine learning. 12th USENIX symposium on operating systems design and implementation (OSDI 16); 2016.

  38. Rieke J, Eitel F, Weygandt M, Haynes J-D, Ritter K. Visualizing convolutional networks for MRI-based diagnosis of Alzheimer’s disease. Understanding and Interpreting Machine Learning in Medical Image Computing Applications: First International Workshops, MLCN 2018, DLF 2018, and iMIMIC 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16–20, 2018, Proceedings 1; 2018: Springer.

  39. Zeiler MD, Fergus R. Visualizing and understanding convolutional networks. Computer Vision–ECCV. 2014: 13th European Conference, Zurich, Switzerland, September 6–12, 2014, Proceedings, Part I 13; 2014: Springer.

  40. Petersen RC, Aisen PS, Beckett LA, Donohue MC, Gamst AC, Harvey DJ, et al. Alzheimer’s disease neuroimaging initiative (ADNI): clinical characterization. Neurology. 2010;74(3):201–9.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Ball MJ, Hachinski V, Fox A, Kirshen AJ, Fisman M, Blume W, et al. A new definition of Alzheimer’s disease: a hippocampal dementia. Lancet. 1985;325(8419):14–6.

    Article  Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

The BioFINDER study was supported by the Alzheimer’s Association (SG-23-1061717), Swedish Research Council (2022 − 00775, 2021–02219), ERA PerMed (ERAPERMED2021-184), the Knut and Alice Wallenberg foundation (2017 − 0383), the Strategic Research Area MultiPark (Multidisciplinary Research in Parkinson’s disease) at Lund University, the Swedish Alzheimer Foundation (AF-980907, AF-980832), the Swedish Brain Foundation (FO2021-0293, FO2023-0163), The Parkinson foundation of Sweden (1412/22), the Cure Alzheimer’s fund, the Konung Gustaf V:s och Drottning Victorias Frimurarestiftelse, the Skåne University Hospital Foundation (2020-O000028), Regionalt Forskningsstöd (2022 − 1259), the Swedish federal government under the ALF agreement (2022-Projekt0080, 2022-Projekt0107), the National Institute of Aging (R01AG083740), EU Joint Programme Neurodegenerative Diseases (2019–03401), and the WASP and DDLS Joint call for research projects (WASP/DDLS22-066). The funding sources had no role in the design and conduct of the study; in the collection, analysis, interpretation of the data; or in the preparation, review, or approval of the manuscript.

Open access funding provided by Lund University.

Author information

Authors and Affiliations

  1. Centre for Mathematical Sciences, Lund University, Lund, Sweden

    Ida Arvidsson, Anders Heyden & Karl Åström

  2. Clinical Memory Research Unit, Department of Clinical Sciences, Malmö, Lund University, Lund, Sweden

    Olof Strandberg, Sebastian Palmqvist, Erik Stomrud, Nicholas Cullen, Shorena Janelidze, Pontus Tideman, Oskar Hansson & Niklas Mattsson-Carlgren

  3. Memory Clinic, Skåne University Hospital, Malmö, Sweden

    Sebastian Palmqvist, Erik Stomrud, Pontus Tideman & Oskar Hansson

  4. Wallenberg Center for Molecular Medicine, Lund University, Lund, Sweden

    Niklas Mattsson-Carlgren

  5. Department of Neurology, Skåne University Hospital, Lund, Sweden

    Niklas Mattsson-Carlgren

Authors
  1. Ida Arvidsson
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Olof Strandberg
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sebastian Palmqvist
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Erik Stomrud
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Nicholas Cullen
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Shorena Janelidze
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Pontus Tideman
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Anders Heyden
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Karl Åström
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Oskar Hansson
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Niklas Mattsson-Carlgren
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

IA, OH and NMC conceptualized and designed the study, with contributions from AH, KÅ, OS and NC. SP, ES, SJ and PT contributed to data acquisition. IA performed the analyses. IA, NMC and NC interpreted results and wrote the first version of the manuscript. NMC supervised the study. All authors contributed to interpretation of the results and critically revised the manuscript for important intellectual content. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Ida Arvidsson or Niklas Mattsson-Carlgren.

Ethics declarations

Ethics approval and consent to participate

The studies were approved by the Ethical Review Authority of Sweden and Region Skåne, and all participants gave written informed consent to participate.

Consent for publication

Not applicable.

Competing interests

OH has acquired research support (for the institution) from ADx, AVID Radiopharmaceuticals, Biogen, Eli Lilly, Eisai, Fujirebio, GE Healthcare, Pfizer, and Roche. In the past 2 years, he has received consultancy/speaker fees from AC Immune, Amylyx, Alzpath, BioArctic, Biogen, Cerveau, Eisai, Eli Lilly, Fujirebio, Merck, Novartis, Novo Nordisk, Roche, Sanofi and Siemens. SP has acquired research support (for the institution) from ki elements / ADDF. In the past 2 years, he has received consultancy/speaker fees from BioArtic, Biogen, Lilly, and Roche.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Arvidsson, I., Strandberg, O., Palmqvist, S. et al. Comparing a pre-defined versus deep learning approach for extracting brain atrophy patterns to predict cognitive decline due to Alzheimer’s disease in patients with mild cognitive symptoms. Alz Res Therapy 16, 61 (2024). https://doi.org/10.1186/s13195-024-01428-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s13195-024-01428-5

Keywords

Alzheimer's Research & Therapy

ISSN: 1758-9193